00549nas a2200133   4500008004100000245009600041210006900137300001100206490000600217100001900223700002300242700002100265856012900286       2023                        eng d00aAttributed Stream Hypergraphs: temporal modeling of node-attributed high-order interactions0 aAttributed Stream Hypergraphs temporal modeling of nodeattribute  a1–190 v81 aFailla, Andrea1 aCitraro, Salvatore1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/attributed-stream-hypergraphs-temporal-modeling-node-attributed-high-order-interactions02085nas a2200265   4500008004100000022001400041245010300055210006900158260001600227490000700243520114900250100002001399700002001419700001801439700002701457700001701484700002501501700001701526700002001543700001901563700002301582700002001605700001901625856017501644       2023                        eng d  a1388-195700aGenerative AI models should include detection mechanisms as a condition for public releaseAbstract0 aGenerative AI models should include detection mechanisms as a co  cJan-12-20230 v253 aThe new wave of ‘foundation models’—general-purpose generative AI models, for production of text (e.g., ChatGPT) or images (e.g., MidJourney)—represent a dramatic advance in the state of the art for AI. But their use also introduces a range of new risks, which has prompted an ongoing conversation about possible regulatory mechanisms. Here we propose a specific principle that should be incorporated into legislation: that any organization developing a foundation model intended for public use must demonstrate a reliable detection mechanism for the content it generates, as a condition of its public release. The detection mechanism should be made publicly available in a tool that allows users to query, for an arbitrary item of content, whether the item was generated (wholly or partly) by the model. In this paper, we argue that this requirement is technically feasible and would play an important role in reducing certain risks from new AI models in many domains. We also outline a number of options for the tool’s design, and summarize a number of points where further input from policymakers and researchers would be required.1 aKnott, Alistair1 aPedreschi, Dino1 aChatila, Raja1 aChakraborti, Tapabrata1 aLeavy, Susan1 aBaeza-Yates, Ricardo1 aEyers, David1 aTrotman, Andrew1 aTeal, Paul, D.1 aBiecek, Przemyslaw1 aRussell, Stuart1 aBengio, Yoshua  uhttps://link.springer.com/article/10.1007/s10676-023-09728-4?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20231028&utm_content=10.1007/s10676-023-09728-400482nas a2200121   4500008004100000245007000041210006800111260001300179100001900192700002200211700002300233856010400256       2022                        eng d00aAttribute-aware Community Events in Feature-rich Dynamic Networks0 aAttributeaware Community Events in Featurerich Dynamic Networks  bSpringer1 aFailla, Andrea1 aMazzoni, Federico1 aCitraro, Salvatore  uhttps://kdd.isti.cnr.it/publications/attribute-aware-community-events-feature-rich-dynamic-networks00549nas a2200121   4500008004100000245012000041210006900161260001300230100001900243700002300262700002100285856012100306       2022                        eng d00aAttributed stream-hypernetwork analysis: homophilic behaviors in pairwise and group political discussions on reddit0 aAttributed streamhypernetwork analysis homophilic behaviors in p  bSpringer1 aFailla, Andrea1 aCitraro, Salvatore1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/attributed-stream-hypernetwork-analysis-homophilic-behaviors-pairwise-and-group01300nas a2200217   4500008004100000020001800041245008900059210006900148260005900217520057700276100002100853700001900874700001600893700002500909700002900934700001700963700002100980700001901001700001801020856004401038       2022                        eng d  a978145039529800aConnected Vehicle Simulation Framework for Parking Occupancy Prediction (Demo Paper)0 aConnected Vehicle Simulation Framework for Parking Occupancy Pre  aNew York, NY, USAbAssociation for Computing Machinery3 aThis paper demonstrates a simulation framework that collects data about connected vehicles' locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications.1 aResce, Pierpaolo1 aVorwerk, Lukas1 aHan, Zhiwei1 aCornacchia, Giuliano1 aAlamdari, Omid, Isfahani1 aNanni, Mirco1 aPappalardo, Luca1 aWeimer, Daniel1 aLiu, Yuanting  uhttps://doi.org/10.1145/3557915.356099501538nas a2200181   4500008004100000020001800041245005000059210005000109260005900159520097300218100002501191700001801216700002001234700001701254700002001271700002101291856004401312       2022                        eng d  a978145039529800aHow Routing Strategies Impact Urban Emissions0 aHow Routing Strategies Impact Urban Emissions  aNew York, NY, USAbAssociation for Computing Machinery3 aNavigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Although undoubtedly useful, navigation apps' impact on the urban environment (e.g., CO2 emissions and pollution) is still largely unclear. In this work, we design a simulation framework to assess the impact of routing algorithms on carbon dioxide emissions within an urban environment. Using APIs from TomTom and OpenStreetMap, we find that settings in which either all vehicles or none of them follow a navigation app's suggestion lead to the worst impact in terms of CO2 emissions. In contrast, when just a portion (around half) of vehicles follow these suggestions, and some degree of randomness is added to the remaining vehicles' paths, we observe a reduction in the overall CO2 emissions over the road network. Our work is a first step towards designing next-generation routing principles that may increase urban well-being while satisfying individual needs.1 aCornacchia, Giuliano1 aBöhm, Matteo1 aMauro, Giovanni1 aNanni, Mirco1 aPedreschi, Dino1 aPappalardo, Luca  uhttps://doi.org/10.1145/3557915.356097701635nas a2200181   4500008004100000245011000041210006900151300001100220490000600231520103600237100002101273700001901294700002001313700002101333700002201354700002201376856005501398       2022                        eng d00aThe long-tail effect of the COVID-19 lockdown on Italians’ quality of life, sleep and physical activity0 alongtail effect of the COVID19 lockdown on Italians quality of l  a1–100 v93 aFrom March 2020 to May 2021, several lockdown periods caused by the COVID-19 pandemic have limited people’s usual activities and mobility in Italy, as well as around the world. These unprecedented confinement measures dramatically modified citizens’ daily lifestyles and behaviours. However, with the advent of summer 2021 and thanks to the vaccination campaign that significantly prevents serious illness and death, and reduces the risk of contagion, all the Italian regions finally returned to regular behaviours and routines. Anyhow, it is unclear if there is a long-tail effect on people’s quality of life, sleep- and physical activity-related behaviours. Thanks to the dataset described in this paper, it will be possible to obtain accurate insights of the changes induced by the lockdown period in the Italians’ health that will permit to provide practical suggestions at local, regional, and state institutions and companies to improve infrastructures and services that could be beneficial to Italians’ well being.1 aNatilli, Michela1 aRossi, Alessio1 aTrecroci, Athos1 aCavaggioni, Luca1 aMerati, Giampiero1 aFormenti, Damiano  uhttps://www.nature.com/articles/s41597-022-01376-502096nas a2200157   4500008004100000245005900041210005900100520156600159100001901725700001901744700002301763700002101786700002201807700002001829856008901849       2022                        eng d00aSemantic Enrichment of XAI Explanations for Healthcare0 aSemantic Enrichment of XAI Explanations for Healthcare3 aExplaining black-box models decisions is crucial to increase doctors' trust in AI-based clinical decision support systems. However, current eXplainable Artificial Intelligence (XAI) techniques usually provide explanations that are not easily understandable by experts outside of AI. Furthermore, most of the them produce explanations that consider only the input features of the algorithm. However, broader information about the clinical context of a patient is usually available even if not processed by the AI-based clinical decision support system for its decision. Enriching the explanations with relevant clinical information concerning the health status of a patient would increase the ability of human experts to assess the reliability of the AI decision. Therefore, in this paper we present a methodology that aims to enable clinical reasoning by semantically enriching AI explanations. Starting from a medical AI explanation based only on the input features provided to the algorithm, our methodology leverages medical ontologies and NLP embedding techniques to link relevant information present in the patient's clinical notes to the original explanation. We validate our methodology with two experiments involving a human expert. Our results highlight promising performance in correctly identifying relevant information about the diseases of the patients, in particular about the associated morphology. This suggests that the presented methodology could be a first step toward developing a natural language explanation of AI decision support systems.1 aCorbucci, Luca1 aMonreale, Anna1 aPanigutti, Cecilia1 aNatilli, Michela1 aSmiraglio, Simona1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/semantic-enrichment-xai-explanations-healthcare00595nas a2200133   4500008004100000245011600041210006900157100002600226700002300252700002100275700002100296700002000317856012400337       2021                        eng d00aCognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities0 aCognitive network science quantifies feelings expressed in suici1 aJoseph, Simmi, Marina1 aCitraro, Salvatore1 aMorini, Virginia1 aRossetti, Giulio1 aStella, Massimo  uhttps://kdd.isti.cnr.it/publications/cognitive-network-science-quantifies-feelings-expressed-suicide-letters-and-reddit01450nas a2200157   4500008004100000020001400041245007600055210006900131260000900200300001000209520096100219100002101180700002301201700001901224856004901243       2021                        eng d  a1941-129400aConformity: a Path-Aware Homophily measure for Node-Attributed Networks0 aConformity a PathAware Homophily measure for NodeAttributed Netw  c2021  a1 - 13 aUnveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Differently from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.1 aRossetti, Giulio1 aCitraro, Salvatore1 aMilli, Letizia  uhttps://ieeexplore.ieee.org/document/932134801670nas a2200181   4500008004100000245006900041210006500110260001600175300001300191490000700204520109000211100002101301700001901322700002101341700001801362700003001380856007801410       2021                        eng d00aExplaining the difference between men’s and women’s football0 aExplaining the difference between men s and women s football  cApr-08-2021  ae02554070 v163 aWomen’s football is gaining supporters and practitioners worldwide, raising questions about what the differences are with men’s football. While the two sports are often compared based on the players’ physical attributes, we analyze the spatio-temporal events during matches in the last World Cups to compare male and female teams based on their technical performance. We train an artificial intelligence model to recognize if a team is male or female based on variables that describe a match’s playing intensity, accuracy, and performance quality. Our model accurately distinguishes between men’s and women’s football, revealing crucial technical differences, which we investigate through the extraction of explanations from the classifier’s decisions. The differences between men’s and women’s football are rooted in play accuracy, the recovery time of ball possession, and the players’ performance quality. Our methodology may help journalists and fans understand what makes women’s football a distinct sport and coaches design tactics tailored to female teams.1 aPappalardo, Luca1 aRossi, Alessio1 aNatilli, Michela1 aCintia, Paolo1 aConstantinou, Anthony, C.  uhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.025540703888nas a2200589   4500008004100000020001400041245010600055210006900161260001500230520215400245100001702399700002302416700002802439700002102467700002202488700001802510700002702528700002302555700001702578700001602595700001802611700002102629700002602650700002102676700002102697700002302718700001802741700001702759700002102776700001802797700001702815700001902832700001702851700002902868700001902897700002102916700001802937700002302955700002202978700002003000700001903020700001903039700002303058700001803081700002403099700001703123700001803140700002203158700002603180700002703206856006503233       2021                        eng d  a1572-843900aGive more data, awareness and control to individual citizens, and they will help COVID-19 containment0 aGive more data awareness and control to individual citizens and   c2021/02/023 aThe rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if and when they want and for specific aims—with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.1 aNanni, Mirco1 aAndrienko, Gennady1 aBarabasi, Albert-Laszlo1 aBoldrini, Chiara1 aBonchi, Francesco1 aCattuto, Ciro1 aChiaromonte, Francesca1 aComandé, Giovanni1 aConti, Marco1 aCoté, Mark1 aDignum, Frank1 aDignum, Virginia1 aDomingo-Ferrer, Josep1 aFerragina, Paolo1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aHelbing, Dirk1 aKaski, Kimmo1 aKertész, János1 aLehmann, Sune1 aLepri, Bruno1 aLukowicz, Paul1 aMatwin, Stan1 aJiménez, David, Megías1 aMonreale, Anna1 aMorik, Katharina1 aOliver, Nuria1 aPassarella, Andrea1 aPasserini, Andrea1 aPedreschi, Dino1 aPentland, Alex1 aPianesi, Fabio1 aPratesi, Francesca1 aRinzivillo, S1 aRuggieri, Salvatore1 aSiebes, Arno1 aTorra, Vicenc1 aTrasarti, Roberto1 avan den Hoven, Jeroen1 aVespignani, Alessandro  uhttps://link.springer.com/article/10.1007/s10676-020-09572-w01965nas a2200145   4500008004100000022001400041245013400055210006900189300000800258490000700266520145600273100002501729700002101754856004401775       2021                        eng d  a2220-996400aA Mechanistic Data-Driven Approach to Synthesize Human Mobility Considering the Spatial, Temporal, and Social Dimensions Together0 aMechanistic DataDriven Approach to Synthesize Human Mobility Con  a5990 v103 aModelling human mobility is crucial in several areas, from urban planning to epidemic modelling, traffic forecasting, and what-if analysis. Existing generative models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected. Those models that capture some social perspectives of human mobility utilize trivial and unrealistic spatial and temporal mechanisms. In this paper, we propose the Spatial, Temporal and Social Exploration and Preferential Return model (STS-EPR), which embeds mechanisms to capture the spatial, temporal, and social aspects together. We compare the trajectories produced by STS-EPR with respect to real-world trajectories and synthetic trajectories generated by two state-of-the-art generative models on a set of standard mobility measures. Our experiments conducted on an open dataset show that STS-EPR, overall, outperforms existing spatial-temporal or social models demonstrating the importance of modelling adequately the sociality to capture precisely all the other dimensions of human mobility. We further investigate the impact of the tile shape of the spatial tessellation on the performance of our model. STS-EPR, which is open-source and tested on open data, represents a step towards the design of a mechanistic data-driven model that captures all the aspects of human mobility comprehensively.1 aCornacchia, Giuliano1 aPappalardo, Luca  uhttps://www.mdpi.com/2220-9964/10/9/59900501nas a2200109   4500008004100000245010900041210006900150260000700219100002500226700002100251856011900272       2021                        eng d00aSTS-EPR: Modelling individual mobility considering the spatial, temporal, and social dimensions together0 aSTSEPR Modelling individual mobility considering the spatial tem  c051 aCornacchia, Giuliano1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/sts-epr-modelling-individual-mobility-considering-spatial-temporal-and-social02976nas a2200373   4500008004100000020002200041245008700063210006900150260005200219520183300271100002102104700001902125700001802144700002002162700002102182700002102203700001902224700002402243700001802267700002302285700001902308700001902327700002102346700001802367700001802385700001902403700002002422700001602442700002002458700001902478700002002497700001802517856006702535       2020                        eng d  a978-3-030-54994-700aAnalysis and Visualization of Performance Indicators in University Admission Tests0 aAnalysis and Visualization of Performance Indicators in Universi  aChambSpringer International Publishingc2020//3 aThis paper presents an analytical platform for evaluation of the performance and anomaly detection of tests for admission to public universities in Italy. Each test is personalized for each student and is composed of a series of questions, classified on different domains (e.g. maths, science, logic, etc.). Since each test is unique for composition, it is crucial to guarantee a similar level of difficulty for all the tests in a session. For this reason, to each question, it is assigned a level of difficulty from a domain expert. Thus, the general difficultness of a test depends on the correct classification of each item. We propose two approaches to detect outliers. A visualization-based approach using dynamic filter and responsive visual widgets. A data mining approach to evaluate the performance of the different questions for five years. We used clustering to group the questions according to a set of performance indicators to provide labeling of the data-driven level of difficulty. The measured level is compared with the a priori assigned by experts. The misclassifications are then highlighted to the expert, who will be able to refine the question or the classification. Sequential pattern mining is used to check if biases are present in the composition of the tests and their performance. This analysis is meant to exclude overlaps or direct dependencies among questions. Analyzing co-occurrences we are able to state that the composition of each test is fair and uniform for all the students, even on several sessions. The analytical results are presented to the expert through a visual web application that loads the analytical data and indicators and composes an interactive dashboard. The user may explore the patterns and models extracted by filtering and changing thresholds and analytical parameters.1 aNatilli, Michela1 aFadda, Daniele1 aRinzivillo, S1 aPedreschi, Dino1 aLicari, Federica1 aSekerinski, Emil1 aMoreira, Nelma1 aOliveira, José, N.1 aRatiu, Daniel1 aGuidotti, Riccardo1 aFarrell, Marie1 aLuckcuck, Matt1 aMarmsoler, Diego1 aCampos, José1 aAstarte, Troy1 aGonnord, Laure1 aCerone, Antonio1 aCouto, Luis1 aDongol, Brijesh1 aKutrib, Martin1 aMonteiro, Pedro1 aDelmas, David  uhttps://link.springer.com/chapter/10.1007/978-3-030-54994-7_1400465nas a2200109   4500008004100000245007600041210006900117100002100186700002300207700001900230856010600249       2020                        eng d00aConformity: A Path-Aware Homophily Measure for Node-Attributed Networks0 aConformity A PathAware Homophily Measure for NodeAttributed Netw1 aRossetti, Giulio1 aCitraro, Salvatore1 aMilli, Letizia  uhttps://kdd.isti.cnr.it/publications/conformity-path-aware-homophily-measure-node-attributed-networks02302nas a2200169   4500008004100000022001400041245012100055210006900176300000900245490000700254520174300261100001902004700002002023700002302043700002002066856004602086       2020                        eng d  a1424-822000aError Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts0 aError Estimation of UltraShort Heart Rate Variability Parameters  a71220 v203 aApplication of ultra&ndash;short Heart Rate Variability (HRV) is desirable in order to increase the applicability of HRV features to wrist-worn wearable devices equipped with heart rate sensors that are nowadays becoming more and more popular in people&rsquo;s daily life. This study is focused in particular on the the two most used HRV parameters, i.e., the standard deviation of inter-beat intervals (SDNN) and the root Mean Squared error of successive inter-beat intervals differences (rMSSD). The huge problem of extracting these HRV parameters from wrist-worn devices is that their data are affected by the motion artifacts. For this reason, estimating the error caused by this huge quantity of missing values is fundamental to obtain reliable HRV parameters from these devices. To this aim, we simulate missing values induced by motion artifacts (from 0 to 70%) in an ultra-short time window (i.e., from 4 min to 30 s) by the random walk Gilbert burst model in 22 young healthy subjects. In addition, 30 s and 2 min ultra-short time windows are required to estimate rMSSD and SDNN, respectively. Moreover, due to the fact that ultra-short time window does not permit assessing very low frequencies, and the SDNN is highly affected by these frequencies, the bias for estimating SDNN continues to increase as the time window length decreases. On the contrary, a small error is detected in rMSSD up to 30 s due to the fact that it is highly affected by high frequencies which are possible to be evaluated even if the time window length decreases. Finally, the missing values have a small effect on rMSSD and SDNN estimation. As a matter of fact, the HRV parameter errors increase slightly as the percentage of missing values increase.1 aRossi, Alessio1 aPedreschi, Dino1 aClifton, David, A.1 aMorelli, Davide  uhttps://www.mdpi.com/1424-8220/20/24/712200474nas a2200109   4500008004100000245008000041210006900121100001900190700002100209700002100230856011300251       2020                        eng d00aEvaluating community detection algorithms for progressively evolving graphs0 aEvaluating community detection algorithms for progressively evol1 aCazabet, Rémy1 aBoudebza, Souaad1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/evaluating-community-detection-algorithms-progressively-evolving-graphs01510nas a2200181   4500008004100000020002200041245004300063210004300106260005200149520094000201100001801141700002301159700001901182700001901201700001901220700002001239856006901259       2020                        eng d  a978-3-030-43823-400aGlobal Explanations with Local Scoring0 aGlobal Explanations with Local Scoring  aChambSpringer International Publishingc2020//3 aArtificial Intelligence systems often adopt machine learning models encoding complex algorithms with potentially unknown behavior. As the application of these “black box” models grows, it is our responsibility to understand their inner working and formulate them in human-understandable explanations. To this end, we propose a rule-based model-agnostic explanation method that follows a local-to-global schema: it generalizes a global explanation summarizing the decision logic of a black box starting from the local explanations of single predicted instances. We define a scoring system based on a rule relevance score to extract global explanations from a set of local explanations in the form of decision rules. Experiments on several datasets and black boxes show the stability, and low complexity of the global explanations provided by the proposed solution in comparison with baselines and state-of-the-art global explainers.1 aSetzu, Mattia1 aGuidotti, Riccardo1 aMonreale, Anna1 aTurini, Franco1 aCellier, Peggy1 aDriessens, Kurt  uhttps://link.springer.com/chapter/10.1007%2F978-3-030-43823-4_1401787nas a2200313   4500008004100000020001400041245004600055210004500101260001500146300001100161520090000172100001701072700002301089700002301112700002101135700001701156700002101173700002301194700002001217700001401237700002901251700002101280700002301301700002001324700002001344700002301364700001901387856006701406       2020                        eng d  a2364-416800aHuman migration: the big data perspective0 aHuman migration the big data perspective  c2020/03/23  a1–203 aHow can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.1 aSirbu, Alina1 aAndrienko, Gennady1 aAndrienko, Natalia1 aBoldrini, Chiara1 aConti, Marco1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aBertoli, Simone1 aKim, Jisu1 aMuntean, Cristina, Ioana1 aPappalardo, Luca1 aPassarella, Andrea1 aPedreschi, Dino1 aPollacci, Laura1 aPratesi, Francesca1 aSharma, Rajesh  uhttps://link.springer.com/article/10.1007%2Fs41060-020-00213-501426nas a2200133   4500008004100000245007500041210006900116300001100185490000600196520096700202100002301169700002101192856007901213       2020                        eng d00aIdentifying and exploiting homogeneous communities in labeled networks0 aIdentifying and exploiting homogeneous communities in labeled ne  a1–200 v53 aAttribute-aware community discovery aims to find well-connected communities that are also homogeneous w.r.t. the labels carried by the nodes. In this work, we address such a challenging task presenting EVA, an algorithmic approach designed to maximize a quality function tailoring both structural and homophilic clustering criteria. We evaluate EVA on several real-world labeled networks carrying both nominal and ordinal information, and we compare our approach to other classic and attribute-aware algorithms. Our results suggest that EVA is the only method, among the compared ones, able to discover homogeneous clusters without considerably degrading partition modularity.We also investigate two well-defined applicative scenarios to characterize better EVA: i) the clustering of a mental lexicon, i.e., a linguistic network modeling human semantic memory, and (ii) the node label prediction task, namely the problem of inferring the missing label of a node.1 aCitraro, Salvatore1 aRossetti, Giulio  uhttps://appliednetsci.springeropen.com/articles/10.1007/s41109-020-00302-102009nas a2200337   4500008004100000020002200041245008900063210006900152260005200221520095500273100002301228700002101251700002101272700001901293700002401312700001801336700002301354700001901377700001901396700002101415700001801436700001801454700001901472700002001491700001601511700002001527700001901547700002001566700001801586856006701604       2020                        eng d  a978-3-030-54994-700a“Know Thyself” How Personal Music Tastes Shape the Last.Fm Online Social Network0 aKnow Thyself How Personal Music Tastes Shape the LastFm Online S  aChambSpringer International Publishingc2020//3 aAs Nietzsche once wrote “Without music, life would be a mistake” (Twilight of the Idols, 1889.). The music we listen to reflects our personality, our way to approach life. In order to enforce self-awareness, we devised a Personal Listening Data Model that allows for capturing individual music preferences and patterns of music consumption. We applied our model to 30k users of Last.Fm for which we collected both friendship ties and multiple listening. Starting from such rich data we performed an analysis whose final aim was twofold: (i) capture, and characterize, the individual dimension of music consumption in order to identify clusters of like-minded Last.Fm users; (ii) analyze if, and how, such clusters relate to the social structure expressed by the users in the service. Do there exist individuals having similar Personal Listening Data Models? If so, are they directly connected in the social graph or belong to the same community?.1 aGuidotti, Riccardo1 aRossetti, Giulio1 aSekerinski, Emil1 aMoreira, Nelma1 aOliveira, José, N.1 aRatiu, Daniel1 aGuidotti, Riccardo1 aFarrell, Marie1 aLuckcuck, Matt1 aMarmsoler, Diego1 aCampos, José1 aAstarte, Troy1 aGonnord, Laure1 aCerone, Antonio1 aCouto, Luis1 aDongol, Brijesh1 aKutrib, Martin1 aMonteiro, Pedro1 aDelmas, David  uhttps://link.springer.com/chapter/10.1007/978-3-030-54994-7_1102283nas a2200265   4500008004100000245013900041210006900180520142700249100001901676700001801695700002201713700001901735700002101754700002501775700001901800700001701819700002101836700002001857700002201877700001801899700002101917700002301938700001901961856003701980       2020                        eng d00aMobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown0 aMobile phone data analytics against the COVID19 epidemics in Ita3 aUnderstanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the epidemics. In this report, we use mobile phone data to infer the movements of people between Italian provinces and municipalities, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the modelling of a risk index tailored for the mobility of each municipality or province. Such an index would be a useful indicator to drive counter-measures in reaction to a sudden reactivation of the epidemics. Mobile phone data, even when aggregated to preserve the privacy of individuals, are a useful data source to track the evolution in time of human mobility, hence allowing for monitoring the effectiveness of control measures such as physical distancing. We address the following analytical questions: How does the mobility structure of a territory change? Do incoming and outcoming flows become more predictable during the lockdown, and what are the differences between weekdays and weekends? Can we detect proper local job markets based on human mobility flows, to eventually shape the borders of a local outbreak?1 aBonato, Pietro1 aCintia, Paolo1 aFabbri, Francesco1 aFadda, Daniele1 aGiannotti, Fosca1 aLopalco, Pier, Luigi1 aMazzilli, Sara1 aNanni, Mirco1 aPappalardo, Luca1 aPedreschi, Dino1 aPenone, Francesco1 aRinzivillo, S1 aRossetti, Giulio1 aSavarese, Marcello1 aTavoschi, Lara  uhttps://arxiv.org/abs/2004.1127800485nas a2200109   4500008004100000245008100041210006900122100002500191700002100216700002100237856011700258       2020                        eng d00aModelling Human Mobility considering Spatial, Temporal and Social Dimensions0 aModelling Human Mobility considering Spatial Temporal and Social1 aCornacchia, Giuliano1 aRossetti, Giulio1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/modelling-human-mobility-considering-spatial-temporal-and-social-dimensions02783nas a2200517   4500008004100000020002200041245008500063210006900148260005200217520120400269100002301473700002501496700002701521700002101548700002101569700001801590700002101608700002201629700001901651700002501670700002301695700002201718700002201740700002101762700001601783700002001799700002601819700002401845700002001869700002201889700002301911700002001934700001901954700002201973700002001995700002002015700002002035700001802055700001902073700002302092700001802115700002002133700002402153700002102177856006702198       2020                        eng d  a978-3-030-65965-300aPrediction and Explanation of Privacy Risk on Mobility Data with Neural Networks0 aPrediction and Explanation of Privacy Risk on Mobility Data with  aChambSpringer International Publishingc2020//3 aThe analysis of privacy risk for mobility data is a fundamental part of any privacy-aware process based on such data. Mobility data are highly sensitive. Therefore, the correct identification of the privacy risk before releasing the data to the public is of utmost importance. However, existing privacy risk assessment frameworks have high computational complexity. To tackle these issues, some recent work proposed a solution based on classification approaches to predict privacy risk using mobility features extracted from the data. In this paper, we propose an improvement of this approach by applying long short-term memory (LSTM) neural networks to predict the privacy risk directly from original mobility data. We empirically evaluate privacy risk on real data by applying our LSTM-based approach. Results show that our proposed method based on a LSTM network is effective in predicting the privacy risk with results in terms of F1 of up to 0.91. Moreover, to explain the predictions of our model, we employ a state-of-the-art explanation algorithm, Shap. We explore the resulting explanation, showing how it is possible to provide effective predictions while explaining them to the end-user.1 aNaretto, Francesca1 aPellungrini, Roberto1 aNardini, Franco, Maria1 aGiannotti, Fosca1 aKoprinska, Irena1 aKamp, Michael1 aAppice, Annalisa1 aLoglisci, Corrado1 aAntonie, Luiza1 aZimmermann, Albrecht1 aGuidotti, Riccardo1 aÖzgöbek, Özlem1 aRibeiro, Rita, P.1 aGavaldà, Ricard1 aGama, João1 aAdilova, Linara1 aKrishnamurthy, Yamuna1 aFerreira, Pedro, M.1 aMalerba, Donato1 aMedeiros, Ibéria1 aCeci, Michelangelo1 aManco, Giuseppe1 aMasciari, Elio1 aRas, Zbigniew, W.1 aChristen, Peter1 aNtoutsi, Eirini1 aSchubert, Erich1 aZimek, Arthur1 aMonreale, Anna1 aBiecek, Przemyslaw1 aRinzivillo, S1 aKille, Benjamin1 aLommatzsch, Andreas1 aGulla, Jon, Atle  uhttps://link.springer.com/chapter/10.1007/978-3-030-65965-3_3401574nas a2200193   4500008004100000020001400041245005500055210005400110260001600164300001100180490000800191520100500199100002301204700002301227700001801250700001901268700002101287856007201308       2020                        eng d  a0169-023X00aPRIMULE: Privacy risk mitigation for user profiles0 aPRIMULE Privacy risk mitigation for user profiles  c2020/01/01/  a1017860 v1253 aThe availability of mobile phone data has encouraged the development of different data-driven tools, supporting social science studies and providing new data sources to the standard official statistics. However, this particular kind of data are subject to privacy concerns because they can enable the inference of personal and private information. In this paper, we address the privacy issues related to the sharing of user profiles, derived from mobile phone data, by proposing PRIMULE, a privacy risk mitigation strategy. Such a method relies on PRUDEnce (Pratesi et al., 2018), a privacy risk assessment framework that provides a methodology for systematically identifying risky-users in a set of data. An extensive experimentation on real-world data shows the effectiveness of PRIMULE strategy in terms of both quality of mobile user profiles and utility of these profiles for analytical services such as the Sociometer (Furletti et al., 2013), a data mining tool for city users classification.1 aPratesi, Francesca1 aGabrielli, Lorenzo1 aCintia, Paolo1 aMonreale, Anna1 aGiannotti, Fosca  uhttps://www.sciencedirect.com/science/article/pii/S0169023X1830534203169nas a2200469   4500008004100000245011000041210006900151520175800220100001801978700001901996700002102015700002102036700002102057700002002078700001802098700001902116700002202135700002202157700002302179700002102202700002702223700001802250700002202268700002102290700002402311700001902335700002202354700001902376700002202395700002302417700002102440700002202461700001902483700002902502700002702531700002302558700002002581700002302601700001802624700002002642856003702662       2020                        eng d00aThe relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy0 arelationship between human mobility and viral transmissibility d3 aWe describe in this report our studies to understand the relationship between human mobility and the spreading of COVID-19, as an aid to manage the restart of the social and economic activities after the lockdown and monitor the epidemics in the coming weeks and months. We compare the evolution (from January to May 2020) of the daily mobility flows in Italy, measured by means of nation-wide mobile phone data, and the evolution of transmissibility, measured by the net reproduction number, i.e., the mean number of secondary infections generated by one primary infector in the presence of control interventions and human behavioural adaptations. We find a striking relationship between the negative variation of mobility flows and the net reproduction number, in all Italian regions, between March 11th and March 18th, when the country entered the lockdown. This observation allows us to quantify the time needed to "switch off" the country mobility (one week) and the time required to bring the net reproduction number below 1 (one week). A reasonably simple regression model provides evidence that the net reproduction number is correlated with a region's incoming, outgoing and internal mobility. We also find a strong relationship between the number of days above the epidemic threshold before the mobility flows reduce significantly as an effect of lockdowns, and the total number of confirmed SARS-CoV-2 infections per 100k inhabitants, thus indirectly showing the effectiveness of the lockdown and the other non-pharmaceutical interventions in the containment of the contagion. Our study demonstrates the value of "big" mobility data to the monitoring of key epidemic indicators to inform choices as the epidemics unfolds in the coming months.1 aCintia, Paolo1 aFadda, Daniele1 aGiannotti, Fosca1 aPappalardo, Luca1 aRossetti, Giulio1 aPedreschi, Dino1 aRinzivillo, S1 aBonato, Pietro1 aFabbri, Francesco1 aPenone, Francesco1 aSavarese, Marcello1 aChecchi, Daniele1 aChiaromonte, Francesca1 aVineis, Paolo1 aGuzzetta, Giorgio1 aRiccardo, Flavia1 aMarziano, Valentina1 aPoletti, Piero1 aTrentini, Filippo1 aBella, Antonio1 aAndrianou, Xanthi1 aDel Manso, Martina1 aFabiani, Massimo1 aBellino, Stefania1 aBoros, Stefano1 aUrdiales, Alberto, Mateo1 aVescio, Maria, Fenicia1 aBrusaferro, Silvio1 aRezza, Giovanni1 aPezzotti, Patrizio1 aAjelli, Marco1 aMerler, Stefano  uhttps://arxiv.org/abs/2006.0314102108nas a2200217   4500008004100000245005300041210005100094520145500145100002301600700002301623700002101646700002201667700001801689700002001707700002101727700002101748700002201769700002301791700001101814856006501825       2020                        eng d00a(So) Big Data and the transformation of the city0 aSo Big Data and the transformation of the city3 aThe exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.1 aAndrienko, Gennady1 aAndrienko, Natalia1 aBoldrini, Chiara1 aCaldarelli, Guido1 aCintia, Paolo1 aCresci, Stefano1 aFacchini, Angelo1 aGiannotti, Fosca1 aGionis, Aristides1 aGuidotti, Riccardo1 aothers  uhttps://link.springer.com/article/10.1007/s41060-020-00207-300533nas a2200121   4500008004100000245009400041210006900135100002100204700001900225700002300244700002100267856012300288       2020                        eng d00aUTLDR: an agent-based framework for modeling infectious diseases and public interventions0 aUTLDR an agentbased framework for modeling infectious diseases a1 aRossetti, Giulio1 aMilli, Letizia1 aCitraro, Salvatore1 aMorini, Virginia  uhttps://kdd.isti.cnr.it/publications/utldr-agent-based-framework-modeling-infectious-diseases-and-public-interventions02991nas a2200157   4500008004100000245013100041210006900172300000900241490000700250520245000257100002002707700001902727700001902746700002202765856004602787       2019                        eng d00aAnalysis of the Impact of Interpolation Methods of Missing RR-intervals Caused by Motion Artifacts on HRV Features Estimations0 aAnalysis of the Impact of Interpolation Methods of Missing RRint  a31630 v193 aWearable physiological monitors have become increasingly popular, often worn during people’s daily life, collecting data 24 hours a day, 7 days a week. In the last decade, these devices have attracted the attention of the scientific community as they allow us to automatically extract information about user physiology (e.g., heart rate, sleep quality and physical activity) enabling inference on their health. However, the biggest issue about the data recorded by wearable devices is the missing values due to motion and mechanical artifacts induced by external stimuli during data acquisition. This missing data could negatively affect the assessment of heart rate (HR) response and estimation of heart rate variability (HRV), that could in turn provide misleading insights concerning the health status of the individual. In this study, we focus on healthy subjects with normal heart activity and investigate the effects of missing variation of the timing between beats (RR-intervals) caused by motion artifacts on HRV features estimation by randomly introducing missing values within a five min time windows of RR-intervals obtained from the nsr2db PhysioNet dataset by using Gilbert burst method. We then evaluate several strategies for estimating HRV in the presence of missing values by interpolating periods of missing values, covering the range of techniques often deployed in the literature, via linear, quadratic, cubic, and cubic spline functions. We thereby compare the HRV features obtained by handling missing data in RR-interval time series against HRV features obtained from the same data without missing values. Finally, we assess the difference between the use of interpolation methods on time (i.e., the timestamp when the heartbeats happen) and on duration (i.e., the duration of the heartbeats), in order to identify the best methodology to handle the missing RR-intervals. The main novel finding of this study is that the interpolation of missing data on time produces more reliable HRV estimations when compared to interpolation on duration. Hence, we can conclude that interpolation on duration modifies the power spectrum of the RR signal, negatively affecting the estimation of the HRV features as the amount of missing values increases. We can conclude that interpolation in time is the optimal method among those considered for handling data with large amounts of missing values, such as data from wearable sensors.1 aMorelli, Davide1 aRossi, Alessio1 aCairo, Massimo1 aClifton, David, A  uhttps://www.mdpi.com/1424-8220/19/14/316301289nas a2200169   4500008004100000020001400041245009500055210006900150260001500219300000700234490000600241520074900247100002100996700001901017700001901036856006401055       2019                        eng d  a2364-822800aCDLIB: a python library to extract, compare and evaluate communities from complex networks0 aCDLIB a python library to extract compare and evaluate communiti  c2019/07/29  a520 v43 aCommunity Discovery is among the most studied problems in complex network analysis. During the last decade, many algorithms have been proposed to address such task; however, only a few of them have been integrated into a common framework, making it hard to use and compare different solutions. To support developers, researchers and practitioners, in this paper we introduce a python library - namely CDlib - designed to serve this need. The aim of CDlib is to allow easy and standardized access to a wide variety of network clustering algorithms, to evaluate and compare the results they provide, and to visualize them. It notably provides the largest available collection of community detection implementations, with a total of 39 algorithms.1 aRossetti, Giulio1 aMilli, Letizia1 aCazabet, Rémy  uhttps://link.springer.com/article/10.1007/s41109-019-0165-901446nas a2200133   4500008004100000245005900041210005900100260001300159300001400172520101900186100001901205700002101224856006701245       2019                        eng d00aChallenges in community discovery on temporal networks0 aChallenges in community discovery on temporal networks  bSpringer  a181–1973 aCommunity discovery is one of the most studied problems in network science. In recent years, many works have focused on discovering communities in temporal networks, thus identifying dynamic communities. Interestingly, dynamic communities are not mere sequences of static ones; new challenges arise from their dynamic nature. Despite the large number of algorithms introduced in the literature, some of these challenges have been overlooked or little studied until recently. In this chapter, we will discuss some of these challenges and recent propositions to tackle them. We will, among other topics, discuss of community events in gradually evolving networks, on the notion of identity through change and the ship of Theseus paradox, on dynamic communities in different types of networks including link streams, on the smoothness of dynamic communities, and on the different types of complexity of algorithms for their discovery. We will also list available tools and libraries adapted to work with this problem.1 aCazabet, Rémy1 aRossetti, Giulio  uhttps://link.springer.com/chapter/10.1007/978-3-030-23495-9_1000443nas a2200097   4500008004100000245008000041210006900121100002300190700002100213856011100234       2019                        eng d00aA complex network approach to semantic spaces: How meaning organizes itself0 acomplex network approach to semantic spaces How meaning organize1 aCitraro, Salvatore1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/complex-network-approach-semantic-spaces-how-meaning-organizes-itself03055nas a2200157   4500008004100000245014800041210006900189300000900258490000700267520245900274100002402733700002802757700001902785700002202804856007102826       2019                        eng d00aDo “girls just wanna have fun”? Participation trends and motivational profiles of women in Norway’s ultimate mass participation ski event0 aDo girls just wanna have fun Participation trends and motivation  a25480 v103 aMass participation sporting events (MPSEs) are viewed as encouraging regular exercise in the population, but concerns have been expressed about the extent to which they are inclusive for women. This study focuses on an iconic cross-country skiing MPSE in Norway, the Birkebeiner race (BR), which includes different variants (main, Friday, half-distance, and women-only races). In order to shed light on women’s participation in this specific MPSE, as well as add to the understanding of women’s MPSEs participation in general, this study was set up to: (i) analyze trends in women’s participation, (ii) examine the characteristics, and (iii) identify key factors characterizing the motivational profile of women in different BR races, with emphasis on the full-distance vs. the women-only races. Entries in the different races throughout the period 1996–2018 were analyzed using an autoregressive model. Information on women’s sociodemographic characteristics, sport and exercise participation, and a range of psychological variables (motives, perceptions, overall satisfaction, and future participation intention) were extracted from a market survey and analyzed using a machine learning (ML) approach (n = 1,149). Additionally, qualitative information generated through open-ended questions was analyzed thematically (n = 116). The relative prevalence of women in the main BR was generally low (< 20%). While the other variants contributed to boosting women’s participation in the overall event, a future increment of women in the main BR was predicted, with women’s ratings possibly matching the men’s by the year 2034. Across all races, most of the women were physically active, of medium-high income, and living in the most urbanized region of Norway. Satisfaction and future participation intention were relatively high, especially among the participants in the women-only races. “Exercise goal” was the predominant participation motive. The participants in women-only races assigned greater importance to social aspects, and perceived the race as a tradition, whereas those in the full-distance races were younger and gave more importance to performance aspects. These findings corroborate known trends and challenges in MPSE participation, but also contribute to greater understanding in this under-researched field. Further research is needed in order to gain more knowledge on how to foster women’s participation in MPSEs.1 aCalogiuri, Giovanna1 aJohansen, Patrick, Foss1 aRossi, Alessio1 aThurston, Miranda  uhttps://www.frontiersin.org/articles/10.3389/fpsyg.2019.02548/full00543nas a2200133   4500008004100000245007400041210007100115100003000186700001700216700002100233700002100254700002100275856011300296       2019                        eng d00aDynComm R Package–Dynamic Community Detection for Evolving Networks0 aDynComm R Package–Dynamic Community Detection for Evolving Netwo1 aSarmento, Rui, Portocarre1 aLemos, Luís1 aCordeiro, Mário1 aRossetti, Giulio1 aCardoso, Douglas  uhttps://kdd.isti.cnr.it/publications/dyncomm-r-package%E2%80%93dynamic-community-detection-evolving-networks01236nas a2200121   4500008004100000245004600041210004400087260001300131520084400144100002300988700002101011856008201032       2019                        eng d00aEva: Attribute-Aware Network Segmentation0 aEva AttributeAware Network Segmentation  bSpringer3 aIdentifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios.1 aCitraro, Salvatore1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/eva-attribute-aware-network-segmentation01257nas a2200133   4500008004100000245009600041210006900137260001300206520077300219100002300992700001901015700002301034856006601057       2019                        eng d00aInvestigating Neighborhood Generation Methods for Explanations of Obscure Image Classifiers0 aInvestigating Neighborhood Generation Methods for Explanations o  bSpringer3 aGiven the wide use of machine learning approaches based on opaque prediction models, understanding the reasons behind decisions of black box decision systems is nowadays a crucial topic. We address the problem of providing meaningful explanations in the widely-applied image classification tasks. In particular, we explore the impact of changing the neighborhood generation function for a local interpretable model-agnostic explanator by proposing four different variants. All the proposed methods are based on a grid-based segmentation of the images, but each of them proposes a different strategy for generating the neighborhood of the image for which an explanation is required. A deep experimentation shows both improvements and weakness of each proposed approach.1 aGuidotti, Riccardo1 aMonreale, Anna1 aCariaggi, Leonardo  uhttps://link.springer.com/chapter/10.1007/978-3-030-16148-4_501999nas a2200181   4500008004100000245011100041210006900152300001100221490000700232520140700239100002101646700001801667700002101685700002301706700002001729700002101749856004701770       2019                        eng d00aPlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach0 aPlayeRank datadriven performance evaluation and player ranking i  a1–270 v103 aThe problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this article, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players’ evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by PlayeRank and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank—i.e. searching players and player versatility—showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.1 aPappalardo, Luca1 aCintia, Paolo1 aFerragina, Paolo1 aMassucco, Emanuele1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://dl.acm.org/doi/abs/10.1145/334317201650nas a2200301   4500008004100000020002200041245004800063210004800111260005200159520072600211100002500937700001900962700002300981700001901004700001901023700001901042700002101061700002001082700002201102700002101124700002301145700002101168700002401189700002301213700002201236700002301258856006701281       2019                        eng d  a978-3-030-13463-100aPrivacy Risk for Individual Basket Patterns0 aPrivacy Risk for Individual Basket Patterns  aChambSpringer International Publishingc2019//3 aRetail data are of fundamental importance for businesses and enterprises that want to understand the purchasing behaviour of their customers. Such data is also useful to develop analytical services and for marketing purposes, often based on individual purchasing patterns. However, retail data and extracted models may also provide very sensitive information to possible malicious third parties. Therefore, in this paper we propose a methodology for empirically assessing privacy risk in the releasing of individual purchasing data. The experiments on real-world retail data show that although individual patterns describe a summary of the customer activity, they may be successful used for the customer re-identifiation.1 aPellungrini, Roberto1 aMonreale, Anna1 aGuidotti, Riccardo1 aAlzate, Carlos1 aMonreale, Anna1 aBioglio, Livio1 aBitetta, Valerio1 aBordino, Ilaria1 aCaldarelli, Guido1 aFerretti, Andrea1 aGuidotti, Riccardo1 aGullo, Francesco1 aPascolutti, Stefano1 aPensa, Ruggero, G.1 aRobardet, Céline1 aSquartini, Tiziano  uhttps://link.springer.com/chapter/10.1007/978-3-030-13463-1_1101689nas a2200193   4500008004100000245007700041210006900118300001100187490000600198520109400204100002101298700001801319700001901337700002301356700002101379700002001400700002101420856005401441       2019                        eng d00aA public data set of spatio-temporal match events in soccer competitions0 apublic data set of spatiotemporal match events in soccer competi  a1–150 v63 aSoccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for scientific research. To fill this gap, this paper describes the largest open collection of soccer-logs ever released, containing all the spatio-temporal events (passes, shots, fouls, etc.) that occured during each match for an entire season of seven prominent soccer competitions. Each match event contains information about its position, time, outcome, player and characteristics. The nature of team sports like soccer, halfway between the abstraction of a game and the reality of complex social systems, combined with the unique size and composition of this dataset, provide an ideal ground for tackling a wide range of data science problems, including the measurement and evaluation of performance, both at individual and at collective level, and the determinants of success and failure.1 aPappalardo, Luca1 aCintia, Paolo1 aRossi, Alessio1 aMassucco, Emanuele1 aFerragina, Paolo1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://www.nature.com/articles/s41597-019-0247-701847nas a2200169   4500008004100000245014600041210006900187300000900256490000600265520126200271100001901533700001801552700002101570700001801591700001901609856004901628       2019                        eng d00aRelationship between External and Internal Workloads in Elite Soccer Players: Comparison between Rate of Perceived Exertion and Training Load0 aRelationship between External and Internal Workloads in Elite So  a51740 v93 aThe use of machine learning (ML) in soccer allows for the management of a large amount of data deriving from the monitoring of sessions and matches. Although the rate of perceived exertion (RPE), training load (S-RPE), and global position system (GPS) are standard methodologies used in team sports to assess the internal and external workload; how the external workload affects RPE and S-RPE remains still unclear. This study explores the relationship between both RPE and S-RPE and the training workload through ML. Data were recorded from 22 elite soccer players, in 160 training sessions and 35 matches during the 2015/2016 season, by using GPS tracking technology. A feature selection process was applied to understand which workload features influence RPE and S-RPE the most. Our results show that the training workloads performed in the previous week have a strong effect on perceived exertion and training load. On the other hand, the analysis of our predictions shows higher accuracy for medium RPE and S-RPE values compared with the extremes. These results provide further evidence of the usefulness of ML as a support to athletic trainers and coaches in understanding the relationship between training load and individual-response in team sports.1 aRossi, Alessio1 aPerri, Enrico1 aPappalardo, Luca1 aCintia, Paolo1 aIaia, Marcello  uhttps://www.mdpi.com/2076-3417/9/23/5174/htm01699nas a2200121   4500008004100000245005400041210005300095490000700148520133500155100002101490700001901511856004701530       2018                        eng d00aCommunity Discovery in Dynamic Networks: a Survey0 aCommunity Discovery in Dynamic Networks a Survey0 v513 aNetworks built to model real world phenomena are characeterised by some properties that have attracted the attention of the scientific community: (i) they are organised according to community structure and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and challenging problem started capturing researcher interest recently: the identification of evolving communities. To model the evolution of a system, dynamic networks can be used: nodes and edges are mutable and their presence, or absence, deeply impacts the community structure that composes them. The aim of this survey is to present the distinctive features and challenges of dynamic community discovery, and propose a classification of published approaches. As a "user manual", this work organizes state of art methodologies into a taxonomy, based on their rationale, and their specific instanciation. Given a desired definition of network dynamics, community characteristics and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers to choose in which direction should future research be oriented. 1 aRossetti, Giulio1 aCazabet, Rémy  uhttps://dl.acm.org/citation.cfm?id=317286701671nas a2200181   4500008004100000245008700041210006900128300001300197490000700210520107500217100001901292700002101311700001801332700001901350700002301369700001901392856007801411       2018                        eng d00aEffective injury forecasting in soccer with GPS training data and machine learning0 aEffective injury forecasting in soccer with GPS training data an  ae02012640 v133 aInjuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer.1 aRossi, Alessio1 aPappalardo, Luca1 aCintia, Paolo1 aIaia, Marcello1 aFernàndez, Javier1 aMedina, Daniel  uhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.020126401833nas a2200169   4500008004100000245005500041210005500096260000700151300000900158490000700167520134100174100002701515700002101542700002301563700002801586856004901614       2018                        eng d00aGravity and scaling laws of city to city migration0 aGravity and scaling laws of city to city migration  c07  a1-190 v133 aModels of human migration provide powerful tools to forecast the flow of migrants, measure the impact of a policy, determine the cost of physical and political frictions and more. Here, we analyse the migration of individuals from and to cities in the US, finding that city to city migration follows scaling laws, so that the city size is a significant factor in determining whether, or not, an individual decides to migrate and the city size of both the origin and destination play key roles in the selection of the destination. We observe that individuals from small cities tend to migrate more frequently, tending to move to similar-sized cities, whereas individuals from large cities do not migrate so often, but when they do, they tend to move to other large cities. Building upon these findings we develop a scaling model which describes internal migration as a two-step decision process, demonstrating that it can partially explain migration fluxes based solely on city size. We then consider the impact of distance and construct a gravity-scaling model by combining the observed scaling patterns with the gravity law of migration. Results show that the scaling laws are a significant feature of human migration and that the inclusion of scaling can overcome the limits of the gravity and the radiation models of human migration.1 aCuriel, Rafael, Prieto1 aPappalardo, Luca1 aGabrielli, Lorenzo1 aBishop, Steven, Richard  uhttps://doi.org/10.1371/journal.pone.019989201581nas a2200397   4500008004100000020002200041245009100063210006900154260004400223300001400267520041400281100001400695700001600709700001700725700001400742700001500756700001600771700002100787700001900808700001700827700001500844700002100859700002000880700002300900700001600923700001800939700002100957700002400978700001901002700001601021700001901037700001801056700001901074700002101093856006901114       2018                        eng d  a978-3-319-61893-700aHow Data Mining and Machine Learning Evolved from Relational Data Base to Data Science0 aHow Data Mining and Machine Learning Evolved from Relational Dat  aChambSpringer International Publishing  a287 - 3063 aDuring the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.1 aAmato, G.1 aCandela, L.1 aCastelli, D.1 aEsuli, A.1 aFalchi, F.1 aGennaro, C.1 aGiannotti, Fosca1 aMonreale, Anna1 aNanni, Mirco1 aPagano, P.1 aPappalardo, Luca1 aPedreschi, Dino1 aPratesi, Francesca1 aRabitti, F.1 aRinzivillo, S1 aRossetti, Giulio1 aRuggieri, Salvatore1 aSebastiani, F.1 aTesconi, M.1 aFlesca, Sergio1 aGreco, Sergio1 aMasciari, Elio1 aSaccà, Domenico  uhttps://link.springer.com/chapter/10.1007%2F978-3-319-61893-7_1702036nas a2200169   4500008004100000245011000041210006900151300001100220490000600231520139500237100002301632700002101655700001901676700002201695700002201717856012701739       2017                        eng d00aAssessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models0 aAssessing the use of mobile phone data to describe recurrent mob  a1609500 v43 aThe recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.1 aPanigutti, Cecilia1 aTizzoni, Michele1 aBajardi, Paolo1 aSmoreda, Zbigniew1 aColizza, Vittoria  uhttps://kdd.isti.cnr.it/publications/assessing-use-mobile-phone-data-describe-recurrent-mobility-patterns-spatial-epidemic01594nas a2200169   4500008004100000245007800041210006900119260001200188300000700200490000600207520108600213100002201299700002201321700001801343700002301361856004001384       2017                        eng d00aDiscovering and Understanding City Events with Big Data: The Case of Rome0 aDiscovering and Understanding City Events with Big Data The Case  c06/2017  a740 v83 aThe increasing availability of large amounts of data and digital footprints has given rise
to ambitious research challenges in many fields, which spans from medical research, financial and
commercial world, to people and environmental monitoring. Whereas traditional data sources and
census fail in capturing actual and up-to-date behaviors, Big Data integrate the missing knowledge
providing useful and hidden information to analysts and decision makers. With this paper, we focus
on the identification of city events by analyzing mobile phone data (Call Detail Record), and we study
and evaluate the impact of these events over the typical city dynamics. We present an analytical
process able to discover, understand and characterize city events from Call Detail Record, designing
a distributed computation to implement Sociometer, that is a profiling tool to categorize phone users.
The methodology provides an useful tool for city mobility manager to manage the events and taking
future decisions on specific classes of users, i.e., residents, commuters and tourists.1 aFurletti, Barbara1 aTrasarti, Roberto1 aCintia, Paolo1 aGabrielli, Lorenzo  uhttps://doi.org/10.3390/info803007401602nas a2200133   4500008004100000245005600041210005600097520113100153100002301284700001901307700002501326700002401351856009301375       2017                        eng d00aEfficiently Clustering Very Large Attributed Graphs0 aEfficiently Clustering Very Large Attributed Graphs3 aAttributed graphs model real networks by enriching their nodes with attributes accounting for properties. Several techniques have been proposed for partitioning these graphs into clusters that are homogeneous with respect to both semantic attributes and to the structure of the graph. However, time and space complexities of state of the art algorithms limit their scalability to medium-sized graphs. We propose SToC (for Semantic-Topological Clustering), a fast and scalable algorithm for partitioning large attributed graphs. The approach is robust, being compatible both with categorical and with quantitative attributes, and it is tailorable, allowing the user to weight the semantic and topological components. Further, the approach does not require the user to guess in advance the number of clusters. SToC relies on well known approximation techniques such as bottom-k sketches, traditional graph-theoretic concepts, and a new perspective on the composition of heterogeneous distance measures. Experimental results demonstrate its ability to efficiently compute high-quality partitions of large scale attributed graphs.1 aBaroni, Alessandro1 aConte, Alessio1 aPatrignani, Maurizio1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/publications/efficiently-clustering-very-large-attributed-graphs02220nas a2200145   4500008004100000245019400041210006900235300001600304490000700320520162400327100002001951700002001971700001901991856006402010       2017                        eng d00aAn empirical verification of a-priori learning models on mailing archives in the context of online learning activities of participants in free\libre open source software (FLOSS) communities0 aempirical verification of apriori learning models on mailing arc  a3207–32290 v223 aFree\Libre Open Source Software (FLOSS) environments are increasingly dubbed as learning environments where practical software engineering skills can be acquired. Numerous studies have extensively investigated how knowledge is acquired in these environments through a collaborative learning model that define a learning process. Such a learning process, identified either as a result of surveys or by means of questionnaires, can be depicted through a series of graphical representations indicating the steps FLOSS community members go through as they acquire and exchange skills. These representations are referred to as a-priori learning models. They are Petri net-like workflow nets (WF-net) that provide a visual representation of the learning process as it is expected to occur. These models are representations of a learning framework or paradigm in FLOSS communities. As such, the credibility of any models is estimated through a process of model verification and validation. Therefore in this paper, we analyze these models in comparison with the real behavior captured in FLOSS repositories by means of conformance verification in process mining. The purpose of our study is twofold. Firstly, the results of our analysis provide insights on the possible discrepancies that are observed between the initial theoretical representations of learning processes and the real behavior captured in FLOSS event logs, constructed from mailing archives. Secondly, this comparison helps foster the understanding on how learning actually takes place in FLOSS environments based on empirical evidence directly from the data.1 aMukala, Patrick1 aCerone, Antonio1 aTurini, Franco  uhttps://link.springer.com/article/10.1007/s10639-017-9573-600434nas a2200109   4500008004100000245006600041210005900107260001900166100002300185700002000208856009600228       2017                        eng d00aOn the Equivalence Between Community Discovery and Clustering0 aEquivalence Between Community Discovery and Clustering  bSpringer, Cham1 aGuidotti, Riccardo1 aCoscia, Michele  uhttps://kdd.isti.cnr.it/publications/equivalence-between-community-discovery-and-clustering01602nas a2200157   4500008004100000245007000041210006800111490000700179520110500186100002201291700001801313700002101331700002001352700002201372856005001394       2017                        eng d00aHyWare: a HYbrid Workflow lAnguage for Research E-infrastructures0 aHyWare a HYbrid Workflow lAnguage for Research Einfrastructures0 v233 aResearch e-infrastructures are "systems of systems", patchworks of tools, services and data sources, evolving over time to address the needs of the scientific process. Accordingly, in such environments, researchers implement their scientific processes by means of workflows made of a variety of actions, including for example usage of web services, download and execution of shared software libraries or tools, or local and manual manipulation of data. Although scientists may benefit from sharing their scientific process, the heterogeneity underpinning e-infrastructures hinders their ability to represent, share and eventually reproduce such workflows. This work presents HyWare, a language for representing scientific process in highly-heterogeneous e-infrastructures in terms of so-called hybrid workflows. HyWare lays in between "business process modeling languages", which offer a formal and high-level description of a reasoning, protocol, or procedure, and "workflow execution languages", which enable the fully automated execution of a sequence of computational steps via dedicated engines.1 aCandela, Leonardo1 aManghi, Paolo1 aGiannotti, Fosca1 aGrossi, Valerio1 aTrasarti, Roberto  uhttp://dx.doi.org/10.1045/january2017-candela01977nas a2200181   4500008004100000245007000041210006900111260001300180300001400193520139800207100002401605700002201629700002001651700002101671700001701692700002001709856006601729       2017                        eng d00aLarge Scale Engagement Through Web-Gaming and Social Computations0 aLarge Scale Engagement Through WebGaming and Social Computations  bSpringer  a237–2543 aIn the last few years the Web has progressively acquired the status of an infrastructure for social computation that allows researchers to coordinate the cognitive abilities of human agents, so to steer the collective user activity towards predefined goals. This general trend is also triggering the adoption of web-games as an alternative laboratory to run experiments in the social sciences and whenever the contribution of human beings can be effectively used for research purposes. Web-games introduce a playful aspect in scientific experiments with the result of increasing participation of people and of keeping their attention steady in time. The aim of this chapter is to suggest a general purpose web-based platform scheme for web-gaming and social computation. This platform will simplify the realization of web-games and will act as a repository of different scientific experiments, thus realizing a sort of showcase that stimulates users’ curiosity and helps researchers in recruiting volunteers. A platform built by following these criteria has been developed within the EveryAware project, the Experimental Tribe (XTribe) platform, which is operational and ready to be used. Finally, a sample web-game hosted by the XTribe platform will be presented with the aim of reporting the results, in terms of participation and motivation, of two different player recruiting strategies.1 aServedio, Vito, D P1 aCaminiti, Saverio1 aGravino, Pietro1 aLoreto, Vittorio1 aSirbu, Alina1 aTria, Francesca  uhttp://link.springer.com/chapter/10.1007/978-3-319-25658-0_1201507nas a2200121   4500008004100000245007100041210006900112300001200181520108400193100002101277700001801298856006901316       2017                        eng d00aQuantifying the relation between performance and success in soccer0 aQuantifying the relation between performance and success in socc  a17500143 aThe availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this study, we analyze more than 6000 games and 10 million events in six European leagues and investigate this relation in soccer competitions. We discover that a team’s position in a competition’s final ranking is significantly related to its typical performance, as described by a set of technical features extracted from the soccer data. Moreover, we find that, while victory and defeats can be explained by the team’s performance during a game, it is difficult to detect draws by using a machine learning approach. We then simulate the outcomes of an entire season of each league only relying on technical data and exploiting a machine learning model trained on data from past seasons. The simulation produces a team ranking which is similar to the actual ranking, suggesting that a complex systems’ view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.1 aPappalardo, Luca1 aCintia, Paolo  uhttp://www.worldscientific.com/doi/abs/10.1142/S021952591750014X01199nas a2200289   4500008004100000022004100041050001400082245004600096210004500142260001200187300000800199490000600207520037500213100002300588700002200611700002300633700002200656700002100678700002000699700002400719700001900743700002000762700002000782700002100802700002400823856006200847       2016                        eng d  aprint: 2095-8099 / online: 2096-0026  a10-1244/N00aBig Data Research in Italy: A Perspective0 aBig Data Research in Italy A Perspective  c06/2016  a1630 v23 aThe aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains.1 aBergamaschi, Sonia1 aCarlini, Emanuele1 aCeci, Michelangelo1 aFurletti, Barbara1 aGiannotti, Fosca1 aMalerba, Donato1 aMezzanzanica, Mario1 aMonreale, Anna1 aPasi, Gabriella1 aPedreschi, Dino1 aPerego, Raffaele1 aRuggieri, Salvatore  uhttp://engineering.org.cn/EN/abstract/article_12288.shtml01694nas a2200157   4500008004100000245006800041210006700109260003800176300001200214520112100226100002301347700002001370700002001390700002401410856010201434       2016                        eng d00aGoing Beyond GDP to Nowcast Well-Being Using Retail Market Data0 aGoing Beyond GDP to Nowcast WellBeing Using Retail Market Data  bSpringer International Publishing  a29–423 aOne of the most used measures of the economic health of a nation is the Gross Domestic Product (GDP): the market value of all officially recognized final goods and services produced within a country in a given period of time. GDP, prosperity and well-being of the citizens of a country have been shown to be highly correlated. However, GDP is an imperfect measure in many respects. GDP usually takes a lot of time to be estimated and arguably the well-being of the people is not quantifiable simply by the market value of the products available to them. In this paper we use a quantification of the average sophistication of satisfied needs of a population as an alternative to GDP. We show that this quantification can be calculated more easily than GDP and it is a very promising predictor of the GDP value, anticipating its estimation by six months. The measure is arguably a more multifaceted evaluation of the well-being of the population, as it tells us more about how people are satisfying their needs. Our study is based on a large dataset of retail micro transactions happening across the Italian territory.1 aGuidotti, Riccardo1 aCoscia, Michele1 aPedreschi, Dino1 aPennacchioli, Diego  uhttps://kdd.isti.cnr.it/publications/going-beyond-gdp-nowcast-well-being-using-retail-market-data01492nas a2200169   4500008004100000020002200041245006500063210006400128520092200192100002001114700002201134700001701156700002001173700002401193700002101217856008401238       2016                        eng d  a978-989-758-181-600aUnveiling Political Opinion Structures with a Web-experiment0 aUnveiling Political Opinion Structures with a Webexperiment3 aThe dynamics of political votes has been widely studied, both for its practical interest and as a paradigm of the dynamics of mass opinions and collective phenomena, where theoretical predictions can be easily tested. However, the vote outcome is often influenced by many factors beyond the bare opinion on the candidate, and in most cases it is bound to a single preference. The voter perception of the political space is still to be elucidated. We here propose a web experiment (laPENSOcos`ı) where we explicitly investigate participants’ opinions on political entities (parties, coalitions, individual candidates) of the Italian political scene. As a main result, we show that the political perception follows a Weber-Fechner-like law, i.e., when ranking political entities according to the user expressed preferences, the perceived distance of the user from a given entity scales as the logarithm of this rank.1 aGravino, Pietro1 aCaminiti, Saverio1 aSirbu, Alina1 aTria, Francesca1 aServedio, Vito, D P1 aLoreto, Vittorio  uhttp://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/000590630039004701914nas a2200145   4500008004100000245005100041210005100092260001600143520143700159100002301596700002001619700002001639700002401659856008501683       2015                        eng d00aBehavioral Entropy and Profitability in Retail0 aBehavioral Entropy and Profitability in Retail  aParisbIEEE3 aHuman behavior is predictable in principle: people are systematic in their everyday choices. This predictability can be used to plan events and infrastructure, both for the public good and for private gains. In this paper we investigate the largely unexplored relationship between the systematic behavior of a customer and its profitability for a retail company. We estimate a customer’s behavioral entropy over two dimensions: the basket entropy is the variety of what customers buy, and the spatio-temporal entropy is the spatial and temporal variety of their shopping sessions. To estimate the basket and the spatiotemporal entropy we use data mining and information theoretic techniques. We find that predictable systematic customers are more profitable for a supermarket: their average per capita expenditures are higher than non systematic customers and they visit the shops more often. However, this higher individual profitability is masked by its overall level. The highly systematic customers are a minority of the customer set. As a consequence, the total amount of revenues they generate is small. We suggest that favoring a systematic behavior in their customers might be a good strategy for supermarkets to increase revenue. These results are based on data coming from a large Italian supermarket chain, including more than 50 thousand customers visiting 23 shops to purchase more than 80 thousand distinct products.1 aGuidotti, Riccardo1 aCoscia, Michele1 aPedreschi, Dino1 aPennacchioli, Diego  uhttps://kdd.isti.cnr.it/publications/behavioral-entropy-and-profitability-retail00450nas a2200133   4500008004100000245008600041210006900127300001400196490000600210100001700216700001800233700002300251856004200274       2015                        eng d00aData Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks0 aData Integration for Microarrays Enhanced Inference for Gene Reg  a255–2690 v41 aSirbu, Alina1 aCrane, Martin1 aRuskin, Heather, J  uhttp://www.mdpi.com/2076-3905/4/2/25500493nas a2200133   4500008004100000245008200041210006900123300001100192100002400203700002200227700003000249700001700279856006300296       2015                        eng d00aEgalitarianism in the rank aggregation problem: a new dimension for democracy0 aEgalitarianism in the rank aggregation problem a new dimension f  a1–161 aContucci, Pierluigi1 aPanizzi, Emanuele1 aRicci-Tersenghi, Federico1 aSirbu, Alina  uhttp://link.springer.com/article/10.1007/s11135-015-0197-x02127nas a2200121   4500008004100000245007900041210006900120520171000189100002001899700002001919700001901939856004701958       2015                        eng d00aAn exploration of learning processes as process maps in FLOSS repositories0 aexploration of learning processes as process maps in FLOSS repos3 aEvidence suggests that Free/Libre Open Source Software (FLOSS) environ-ments provide unlimited learning opportunities. Community members engage in a number of activities both during their interaction with their peers and while mak-ing use of the tools available in these environments. A number of studies docu-ment the existence of learning processes in FLOSS through the analysis of sur-veys and questionnaires filled by FLOSS project participants. At the same time, the interest in understanding the dynamics of the FLOSS phenomenon, its popu-larity and success resulted in the development of tools and techniques for extract-ing and analyzing data from different FLOSS data sources. This new field is called Mining Software Repositories (MSR). In spite of these efforts, there is limited work aiming to provide empirical evidence of learning processes directly from FLOSS repositories. In this paper, we seek to trigger such an initiative by proposing an approach based on Process Mining to trace learning behaviors from FLOSS participants’ trails of activities, as recorded in FLOSS repositories, and visualize them as pro-cess maps. Process maps provide a pictorial representation of real behavior as it is recorded in FLOSS data. Our aim is to provide critical evidence that boosts the understanding of learning behavior in FLOSS communities by analyzing the rel-evant repositories. In order to accomplish this, we propose an effective approach that comprises first the mining of FLOSS repositories in order to generate Event logs, and then the generation of process maps, equipped with relevant statistical data interpreting and indicating the value of process discovery from these reposi-tories.1 aMukala, Patrick1 aCerone, Antonio1 aTurini, Franco  uhttp://eprints.adm.unipi.it/id/eprint/234402029nas a2200145   4500008004100000245008700041210006900128520139000197100001801587700002101605700002001626700002101646700002001667856019601687       2015                        eng d00aThe harsh rule of the goals: data-driven performance indicators for football teams0 aharsh rule of the goals datadriven performance indicators for fo3 a—Sports analytics in general, and football (soccer in
USA) analytics in particular, have evolved in recent years in an
amazing way, thanks to automated or semi-automated sensing
technologies that provide high-fidelity data streams extracted
from every game. In this paper we propose a data-driven
approach and show that there is a large potential to boost the
understanding of football team performance. From observational
data of football games we extract a set of pass-based performance
indicators and summarize them in the H indicator. We observe a
strong correlation among the proposed indicator and the success
of a team, and therefore perform a simulation on the four major
European championships (78 teams, almost 1500 games). The
outcome of each game in the championship was replaced by a
synthetic outcome (win, loss or draw) based on the performance
indicators computed for each team. We found that the final
rankings in the simulated championships are very close to the
actual rankings in the real championships, and show that teams
with high ranking error show extreme values of a defense/attack
efficiency measure, the Pezzali score. Our results are surprising
given the simplicity of the proposed indicators, suggesting that
a complex systems’ view on football data has the potential of
revealing hidden patterns and behavior of superior quality.1 aCintia, Paolo1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca1 aMalvaldi, Marco  uhttps://www.researchgate.net/profile/Luca_Pappalardo/publication/281318318_The_harsh_rule_of_the_goals_data-driven_performance_indicators_for_football_teams/links/561668e308ae37cfe4090a5d.pdf01355nas a2200133   4500008004100000245005800041210005800099260001900157520089600176100002001072700002001092700001901112856009001131       2015                        eng d00aMining learning processes from FLOSS mailing archives0 aMining learning processes from FLOSS mailing archives  bSpringer, Cham3 aEvidence suggests that Free/Libre Open Source Software (FLOSS) environments provide unlimited learning opportunities. Community members engage in a number of activities both during their interaction with their peers and while making use of these environments. As FLOSS repositories store data about participants’ interaction and activities, we analyze participants’ interaction and knowledge exchange in emails to trace learning activities that occur in distinct phases of the learning process. We make use of semantic search in SQL to retrieve data and build corresponding event logs which are then fed to a process mining tool in order to produce visual workflow nets. We view these nets as representative of the traces of learning activities in FLOSS as well as their relevant flow of occurrence. Additional statistical details are provided to contextualize and describe these models.1 aMukala, Patrick1 aCerone, Antonio1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/mining-learning-processes-floss-mailing-archives02544nas a2200373   4500008004100000022001400041245008200055210006900137260000900206300001300215490000700228520142000235100001701655700001901672700002201691700002201713700001501735700002001750700002001770700001901790700002101809700002101830700001901851700002101870700001601891700002601907700002001933700002401953700001701977700001701994700002002011700002702031856011202058       2015                        eng d  a1932-620300aParticipatory Patterns in an International Air Quality Monitoring Initiative.0 aParticipatory Patterns in an International Air Quality Monitorin  c2015  ae01367630 v103 a<p>The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.</p>1 aSirbu, Alina1 aBecker, Martin1 aCaminiti, Saverio1 aDe Baets, Bernard1 aElen, Bart1 aFrancis, Louise1 aGravino, Pietro1 aHotho, Andreas1 aIngarra, Stefano1 aLoreto, Vittorio1 aMolino, Andrea1 aMueller, Juergen1 aPeters, Jan1 aRicchiuti, Ferdinando1 aSaracino, Fabio1 aServedio, Vito, D P1 aStumme, Gerd1 aTheunis, Jan1 aTria, Francesca1 aVan den Bossche, Joris  uhttps://kdd.isti.cnr.it/publications/participatory-patterns-international-air-quality-monitoring-initiative02010nas a2200157   4500008004100000245004500041210004500086260001200131300001100143490000600154520154300160100002001703700002401723700002101747856008401768       2015                        eng d00aProduct assortment and customer mobility0 aProduct assortment and customer mobility  c10-2015  a1–180 v43 aCustomers mobility is dependent on the sophistication of their needs: sophisticated customers need to travel more to fulfill their needs. In this paper, we provide more detailed evidence of this phenomenon, providing an empirical validation of the Central Place Theory. For each customer, we detect what is her favorite shop, where she purchases most products. We can study the relationship between the favorite shop and the closest one, by recording the influence of the shop’s size and the customer’s sophistication in the discordance cases, i.e. the cases in which the favorite shop is not the closest one. We show that larger shops are able to retain most of their closest customers and they are able to catch large portions of customers from smaller shops around them. We connect this observation with the shop’s larger sophistication, and not with its other characteristics, as the phenomenon is especially noticeable when customers want to satisfy their sophisticated needs. This is a confirmation of the recent extensions of the Central Place Theory, where the original assumptions of homogeneity in customer purchase power and needs are challenged. Different types of shops have also different survival logics. The largest shops get closed if they are unable to catch customers from the smaller shops, while medium size shops get closed if they cannot retain their closest customers. All analysis are performed on a large real-world dataset recording all purchases from millions of customers across the west coast of Italy.1 aCoscia, Michele1 aPennacchioli, Diego1 aGiannotti, Fosca  uhttp://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-015-0051-301825nas a2200217   4500008004100000245004800041210004600089300000600135490000600141520119400147100001801341700001901359700002201378700002401400700002101424700002001445700002301465700001901488700002401507856007601531       2015                        eng d00aA risk model for privacy in trajectory data0 arisk model for privacy in trajectory data  a90 v23 aTime sequence data relating to users, such as medical histories and mobility data, are good candidates for data mining, but often contain highly sensitive information. Different methods in privacy-preserving data publishing are utilised to release such private data so that individual records in the released data cannot be re-linked to specific users with a high degree of certainty. These methods provide theoretical worst-case privacy risks as measures of the privacy protection that they offer. However, often with many real-world data the worst-case scenario is too pessimistic and does not provide a realistic view of the privacy risks: the real probability of re-identification is often much lower than the theoretical worst-case risk. In this paper, we propose a novel empirical risk model for privacy which, in relation to the cost of privacy attacks, demonstrates better the practical risks associated with a privacy preserving data release. We show detailed evaluation of the proposed risk model by using k-anonymised real-world mobility data and then, we show how the empirical evaluation of the privacy risk has a different trend in synthetic data describing random movements.1 aBasu, Anirban1 aMonreale, Anna1 aTrasarti, Roberto1 aCorena, Juan Camilo1 aGiannotti, Fosca1 aPedreschi, Dino1 aKiyomoto, Shinsaku1 aMiyake, Yutaka1 aYanagihara, Tadashi  uhttps://kdd.isti.cnr.it/publications/risk-model-privacy-trajectory-data00491nas a2200121   4500008004100000245006900041210006900110260003100179300001400210100002300224700001800247856010400265       2015                        eng d00aTowards a Boosted Route Planner Using Individual Mobility Models0 aTowards a Boosted Route Planner Using Individual Mobility Models  bSpringer Berlin Heidelberg  a108–1231 aGuidotti, Riccardo1 aCintia, Paolo  uhttps://kdd.isti.cnr.it/publications/towards-boosted-route-planner-using-individual-mobility-models01426nas a2200133   4500008004100000245009200041210006900133260001900202520089400221100002001115700002001135700001901155856011801174       2014                        eng d00aAn abstract state machine (ASM) representation of learning process in FLOSS communities0 aabstract state machine ASM representation of learning process in  bSpringer, Cham3 aFree/Libre Open Source Software (FLOSS) communities as collaborative environments enable the occurrence of learning between participants in these groups. With the increasing interest research on understanding the mechanisms and processes through which learning occurs in FLOSS, there is an imperative to describe these processes. One successful way of doing this is through specification methods. In this paper, we describe the adoption of Abstract States Machines (ASMs) as a specification methodology for the description of learning processes in FLOSS. The goal of this endeavor is to represent the many possible steps and/or activities FLOSS participants go through during interactions that can be categorized as learning processes. Through ASMs, we express learning phases as states while activities that take place before moving from one state to another are expressed as transitions.1 aMukala, Patrick1 aCerone, Antonio1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/abstract-state-machine-asm-representation-learning-process-floss-communities01965nas a2200193   4500008004100000245007600041210006900117260000900186520136400195100001801559700002401577700001901601700002001620700002101640700002301661700002001684700001901704856004801723       2014                        eng d00aCF-inspired Privacy-Preserving Prediction of Next Location in the Cloud0 aCFinspired PrivacyPreserving Prediction of Next Location in the   bIEEE3 aMobility data gathered from location sensors such as Global Positioning System (GPS) enabled phones and vehicles is valuable for spatio-temporal data mining for various location-based services (LBS). Such data is often considered sensitive and there exist many a mechanism for privacy preserving analyses of the data. Through various anonymisation mechanisms, it can be ensured with a high probability that a particular individual cannot be identified when mobility data is outsourced to third parties for analysis. However, challenges remain with the privacy of the queries on outsourced analysis results, especially when the queries are sent directly to third parties by end-users. Drawing inspiration from our earlier work in privacy preserving collaborative filtering (CF) and next location prediction, in this exploratory work, we propose a novel representation of trajectory data in the CF domain and experiment with a privacy preserving Slope One CF predictor. We present evaluations for the accuracy and the computational performance of our proposal using anonymised data gathered from real traffic data in the Italian cities of Pisa and Milan. One use-case is a third-party location-prediction-as-a-service deployed on a public cloud, which can respond to privacy-preserving queries while enabling data owners to build a rich predictor on the cloud. 1 aBasu, Anirban1 aCorena, Juan Camilo1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca1 aKiyomoto, Shinsaku1 aVaidya, Jaideep1 aMiyake, Yutaka  uhttp://dx.doi.org/10.1109/CloudCom.2014.11400546nas a2200145   4500008004100000245007400041210006900115490000700184100001900191700002400210700002000234700002400254700002200278856010000300       2014                        eng d00aThe CoLing Lab system for Sentiment Polarity Classification of tweets0 aCoLing Lab system for Sentiment Polarity Classification of tweet0 vII1 aPassaro, Lucia1 aLebani, Gianluca, E1 aPollacci, Laura1 aChersoni, Emmanuele1 aLenci, Alessandro  uhttps://kdd.isti.cnr.it/publications/coling-lab-system-sentiment-polarity-classification-tweets01871nas a2200217   4500008003900000022001400039245010000053210006900153300000600222520115600228653001901384100002201403700003101425700001701456700002201473700002201495700002101517700002201538700002201560856007101582       2014                          d  a0308-596100aDiscovering urban and country dynamics from mobile phone data with spatial correlation patterns0 aDiscovering urban and country dynamics from mobile phone data wi  a-3 aAbstract Mobile communication technologies pervade our society and existing wireless networks are able to sense the movement of people, generating large volumes of data related to human activities, such as mobile phone call records. At the present, this kind of data is collected and stored by telecom operators infrastructures mainly for billing reasons, yet it represents a major source of information in the study of human mobility. In this paper, we propose an analytical process aimed at extracting interconnections between different areas of the city that emerge from highly correlated temporal variations of population local densities. To accomplish this objective, we propose a process based on two analytical tools: (i) a method to estimate the presence of people in different geographical areas; and (ii) a method to extract time- and space-constrained sequential patterns capable to capture correlations among geographical areas in terms of significant co-variations of the estimated presence. The methods are presented and combined in order to deal with two real scenarios of different spatial scale: the Paris Region and the whole France.10aUrban dynamics1 aTrasarti, Roberto1 aOlteanu-Raimond, Ana-Maria1 aNanni, Mirco1 aCouronné, Thomas1 aFurletti, Barbara1 aGiannotti, Fosca1 aSmoreda, Zbigniew1 aZiemlicki, Cezary  uhttp://www.sciencedirect.com/science/article/pii/S030859611300201200512nas a2200133   4500008004100000245009000041210007100131260003800202300001400240100001700254700001800271700002300289856006600312       2014                        eng d00aEGIA–Evolutionary Optimisation of Gene Regulatory Networks, an Integrative Approach0 aEGIA–Evolutionary Optimisation of Gene Regulatory Networks an In  bSpringer International Publishing  a217–2291 aSirbu, Alina1 aCrane, Martin1 aRuskin, Heather, J  uhttp://link.springer.com/chapter/10.1007/978-3-319-05401-8_2100447nas a2200109   4500008004100000245006800041210006700109100001800176700002100194700002000215856010200235       2014                        eng d00aMining efficient training patterns of non-professional cyclists0 aMining efficient training patterns of nonprofessional cyclists1 aCintia, Paolo1 aPappalardo, Luca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/mining-efficient-training-patterns-non-professional-cyclists01818nas a2200205   4500008003900000245002300039210002300062260001500085300000900100520128200109100001701391700002201408700001801430700002201448700001801470700002301488700001801511700002101529856006201550       2014                          d00aMobility Profiling0 aMobility Profiling  bIGI Global  a1-293 aThe ability to understand the dynamics of human mobility is crucial for tasks like urban planning and transportation management. The recent rapidly growing availability of large spatio-temporal datasets gives us the possibility to develop sophisticated and accurate analysis methods and algorithms that can enable us to explore several relevant mobility phenomena: the distinct access paths to a territory, the groups of persons that move together in space and time, the regions of a territory that contains a high density of traffic demand, etc. All these paradigmatic perspectives focus on a collective view of the mobility where the interesting phenomenon is the result of the contribution of several moving objects. In this chapter, the authors explore a different approach to the topic and focus on the analysis and understanding of relevant individual mobility habits in order to assign a profile to an individual on the basis of his/her mobility. This process adds a semantic level to the raw mobility data, enabling further analyses that require a deeper understanding of the data itself. The studies described in this chapter are based on two large datasets of spatio-temporal data, originated, respectively, from GPS-equipped devices and from a mobile phone network. 1 aNanni, Mirco1 aTrasarti, Roberto1 aCintia, Paolo1 aFurletti, Barbara1 aRenso, Chiara1 aGabrielli, Lorenzo1 aRinzivillo, S1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mobility-profiling-001500nas a2200133   4500008004100000245007000041210006900111260001900180520100900199100002001208700002001228700001901248856009901267       2014                        eng d00aOntolifloss: Ontology for learning processes in FLOSS communities0 aOntolifloss Ontology for learning processes in FLOSS communities  bSpringer, Cham3 aFree/Libre Open Source Software (FLOSS) communities are considered an example of commons-based peer-production models where groups of participants work together to achieve projects of common purpose. In these settings, many occurring activities can be documented and have established them as learning environments. As knowledge exchange is proved to occur in FLOSS, the dynamic and free nature of participation poses a great challenge in understanding activities pertaining to Learning Processes.

In this paper we raise this question and propose an ontology (called OntoLiFLOSS) in order to define terms and concepts that can explain learning activities taking place in these communities. The objective of this endeavor is to define in the simplest possible way a common definition of concepts and activities that can guide the identification of learning processes taking place among FLOSS members in any of the standard repositories such as mailing list, SVN, bug trackers and even discussion forums.1 aMukala, Patrick1 aCerone, Antonio1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/ontolifloss-ontology-learning-processes-floss-communities01798nas a2200133   4500008004100000245011000041210006900151260000900220520130600229100002401535700002001559700002001579856006501599       2014                        eng d00aOverlap versus partition: marketing classification and customer profiling in complex networks of products0 aOverlap versus partition marketing classification and customer p  bIEEE3 aIn recent years we witnessed the explosion in the availability of data regarding human and customer behavior in the market. This data richness era has fostered the development of useful applications in understanding how markets and the minds of the customers work. In this paper we focus on the analysis of complex networks based on customer behavior. Complex network analysis has provided a new and wide toolbox for the classic data mining task of clustering. With community discovery, i.e. the detection of functional modules in complex networks, we are now able to group together customers and products using a variety of different criteria. The aim of this paper is to explore this new analytic degree of freedom. We are interested in providing a case study uncovering the meaning of different community discovery algorithms on a network of products connected together because co-purchased by the same customers. We focus our interest in the different interpretation of a partition approach, where each product belongs to a single community, against an overlapping approach, where each product can belong to multiple communities. We found that the former is useful to improve the marketing classification of products, while the latter is able to create a collection of different customer profiles.1 aPennacchioli, Diego1 aCoscia, Michele1 aPedreschi, Dino  uhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=681831200482nas a2200145   4500008004100000245006800041210006300109100002400172700002100196700002100217700002000238700002100258700002000279856003700299       2014                        eng d00aThe patterns of musical influence on the Last.Fm social network0 apatterns of musical influence on the LastFm social network1 aPennacchioli, Diego1 aRossetti, Giulio1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca1 aCoscia, Michele  uhttps://kdd.isti.cnr.it/node/62301645nas a2200205   4500008003900000245004500039210004300084300001400127520105800141100001801199700001901217700002401236700002101260700002001281700002301301700001901324700002401343700002201367856005001389       2014                          d00aA Privacy Risk Model for Trajectory Data0 aPrivacy Risk Model for Trajectory Data  a125–1403 aTime sequence data relating to users, such as medical histories and mobility data, are good candidates for data mining, but often contain highly sensitive information. Different methods in privacy-preserving data publishing are utilised to release such private data so that individual records in the released data cannot be re-linked to specific users with a high degree of certainty. These methods provide theoretical worst-case privacy risks as measures of the privacy protection that they offer. However, often with many real-world data the worst-case scenario is too pessimistic and does not provide a realistic view of the privacy risks: the real probability of re-identification is often much lower than the theoretical worst-case risk. In this paper we propose a novel empirical risk model for privacy which, in relation to the cost of privacy attacks, demonstrates better the practical risks associated with a privacy preserving data release. We show detailed evaluation of the proposed risk model by using k-anonymised real-world mobility data.1 aBasu, Anirban1 aMonreale, Anna1 aCorena, Juan Camilo1 aGiannotti, Fosca1 aPedreschi, Dino1 aKiyomoto, Shinsaku1 aMiyake, Yutaka1 aYanagihara, Tadashi1 aTrasarti, Roberto  uhttp://dx.doi.org/10.1007/978-3-662-43813-8_901411nas a2200133   4500008004100000245008100041210006900122260001900191520090300210100002001113700002001133700001901153856010501172       2014                        eng d00aProcess mining event logs from FLOSS data: state of the art and perspectives0 aProcess mining event logs from FLOSS data state of the art and p  bSpringer, Cham3 aFree/Libre Open Source Software (FLOSS) is a phenomenon that has undoubtedly triggered extensive research endeavors. At the heart of these initiatives is the ability to mine data from FLOSS repositories with the hope of revealing empirical evidence to answer existing questions on the FLOSS development process. In spite of the success produced with existing mining techniques, emerging questions about FLOSS data require alternative and more appropriate ways to explore and analyse such data.

In this paper, we explore a different perspective called process mining. Process mining has been proved to be successful in terms of tracing and reconstructing process models from data logs (event logs). The chief objective of our analysis is threefold. We aim to achieve: (1) conformance to predefined models; (2) discovery of new model patterns; and, finally, (3) extension to predefined models.

1 aMukala, Patrick1 aCerone, Antonio1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/process-mining-event-logs-floss-data-state-art-and-perspectives02340nas a2200169   4500008004100000245004200041210003800083300001100121490000600132520186000138100002401998700002002022700001802042700002102060700002002081856006902101       2014                        eng d00aThe retail market as a complex system0 aretail market as a complex system  a1–270 v33 aAim of this paper is to introduce the complex system perspective into retail market analysis. Currently, to understand the retail market means to search for local patterns at the micro level, involving the segmentation, separation and profiling of diverse groups of consumers. In other contexts, however, markets are modelled as complex systems. Such strategy is able to uncover emerging regularities and patterns that make markets more predictable, e.g. enabling to predict how much a country’s GDP will grow. Rather than isolate actors in homogeneous groups, this strategy requires to consider the system as a whole, as the emerging pattern can be detected only as a result of the interaction between its self-organizing parts. This assumption holds also in the retail market: each customer can be seen as an independent unit maximizing its own utility function. As a consequence, the global behaviour of the retail market naturally emerges, enabling a novel description of its properties, complementary to the local pattern approach. Such task demands for a data-driven empirical framework. In this paper, we analyse a unique transaction database, recording the micro-purchases of a million customers observed for several years in the stores of a national supermarket chain. We show the emergence of the fundamental pattern of this complex system, connecting the products’ volumes of sales with the customers’ volumes of purchases. This pattern has a number of applications. We provide three of them. By enabling us to evaluate the sophistication of needs that a customer has and a product satisfies, this pattern has been applied to the task of uncovering the hierarchy of needs of the customers, providing a hint about what is the next product a customer could be interested in buying and predicting in which shop she is likely to go to buy it.1 aPennacchioli, Diego1 aCoscia, Michele1 aRinzivillo, S1 aGiannotti, Fosca1 aPedreschi, Dino  uhttp://link.springer.com/article/10.1140/epjds/s13688-014-0033-x02438nas a2200157   4500008004100000245008400041210006900125300000600194490000600200520195300206100002002159700002102179700002102200700002002221856003902241       2014                        eng d00aUncovering Hierarchical and Overlapping Communities with a Local-First Approach0 aUncovering Hierarchical and Overlapping Communities with a Local  a60 v93 aCommunity discovery in complex networks is the task of organizing a network’s structure by grouping together nodes related to each other. Traditional approaches are based on the assumption that there is a global-level organization in the network. However, in many scenarios, each node is the bearer of complex information and cannot be classified in disjoint clusters. The top-down global view of the partition approach is not designed for this. Here, we represent this complex information as multiple latent labels, and we postulate that edges in the networks are created among nodes carrying similar labels. The latent labels are the communities a node belongs to and we discover them with a simple local-first approach to community discovery. This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, its ego neighborhood, using a label propagation algorithm, assuming that each node is aware of the label it shares with each of its connections. The local communities are merged hierarchically, unveiling the modular organization of the network at the global level and identifying overlapping groups and groups of groups. We tested this intuition against the state-of-the-art overlapping community discovery and found that our new method advances in the chosen scenarios in the quality of the obtained communities. We perform a test on benchmark and on real-world networks, evaluating the quality of the community coverage by using the extracted communities to predict the metadata attached to the nodes, which we consider external information about the latent labels. We also provide an explanation about why real-world networks contain overlapping communities and how our logic is able to capture them. Finally, we show how our method is deterministic, is incremental, and has a limited time complexity, so that it can be used on real-world scale networks.1 aCoscia, Michele1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttp://doi.acm.org/10.1145/262951100515nas a2200121   4500008003900000245007700039210006900116100002200185700002300207700001800230700003800248856010700286       2013                          d00aAverage Speed Estimation For Road Networks Based On GPS Raw Trajectories0 aAverage Speed Estimation For Road Networks Based On GPS Raw Traj1 aBarbosa, Ivanildo1 aCasanova, Marco, A1 aRenso, Chiara1 aMacêdo, José Antônio Fernandes  uhttps://kdd.isti.cnr.it/publications/average-speed-estimation-road-networks-based-gps-raw-trajectories02195nas a2200289   4500008004100000022001400041245005900055210005800114260000900172300001100181490000600192520133700198100001901535700002201554700002101576700002001597700002001617700002801637700001901665700002101684700002101705700002601726700002401752700001701776700002001793856009201813       2013                        eng d  a1932-620300aAwareness and learning in participatory noise sensing.0 aAwareness and learning in participatory noise sensing  c2013  ae816380 v83 a<p>The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.</p>1 aBecker, Martin1 aCaminiti, Saverio1 aFiorella, Donato1 aFrancis, Louise1 aGravino, Pietro1 aHaklay, Mordechai, Muki1 aHotho, Andreas1 aLoreto, Vittorio1 aMueller, Juergen1 aRicchiuti, Ferdinando1 aServedio, Vito, D P1 aSirbu, Alina1 aTria, Francesca  uhttps://kdd.isti.cnr.it/publications/awareness-and-learning-participatory-noise-sensing00410nas a2200109   4500008004100000245008700041210006900128100001800197700002100215700002000236856004400256       2013                        eng d00a"Engine Matters": {A} First Large Scale Data Driven Study on Cyclists' Performance0 aEngine Matters A First Large Scale Data Driven Study on Cyclists1 aCintia, Paolo1 aPappalardo, Luca1 aPedreschi, Dino  uhttp://dx.doi.org/10.1109/ICDMW.2013.4101933nas a2200169   4500008003900000245004700039210004600086300001200132490000700144520146600151100002501617700002001642700002101662700001901683700002001702856004101722       2013                          d00aEvolving networks: Eras and turning points0 aEvolving networks Eras and turning points  a27–480 v173 aWithin the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolution on the global and the local scale, extracting properties of either the entire network or local patterns. In this paper, we focus on detecting clusters of temporal snapshots of a network, to be interpreted as eras of evolution. To this aim, we introduce a novel hierarchical clustering methodology, based on a dissimilarity measure (derived from the Jaccard coefficient) between two temporal snapshots of the network, able to detect the turning points at the beginning of the eras. We devise a framework to discover and browse the eras, either in top-down or a bottom-up fashion, supporting the exploration of the evolution at any level of temporal resolution. We show how our approach applies to real networks and null models, by detecting eras in an evolving co-authorship graph extracted from a bibliographic dataset, a collaboration graph extracted from a cinema database, and a network extracted from a database of terrorist attacks; we illustrate how the discovered temporal clustering highlights the crucial moments when the networks witnessed profound changes in their structure. Our approach is finally boosted by introducing a meaningful labeling of the obtained clusters, such as the characterizing topics of each discovered era, thus adding a semantic dimension to our analysis.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttp://dx.doi.org/10.3233/IDA-12056600477nas a2200133   4500008003900000245005700039210005700096100002400153700002000177700001800197700002000215700002100235856008700256       2013                          d00aExplaining the PRoduct Range Effect in Purchase Data0 aExplaining the PRoduct Range Effect in Purchase Data1 aPennacchioli, Diego1 aCoscia, Michele1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/explaining-product-range-effect-purchase-data00445nas a2200133   4500008004100000245005900041210005700100100001800157700002200175700003400197700001800231700002000249856004200269       2013                        eng d00aA Gravity Model for Speed Estimation over Road Network0 aGravity Model for Speed Estimation over Road Network1 aCintia, Paolo1 aTrasarti, Roberto1 ade Macêdo, José, Antônio F1 aAlmada, Livia1 aFereira, Camila  uhttp://dx.doi.org/10.1109/MDM.2013.8300432nas a2200121   4500008003900000245005500039210005500094260001600149100001800165700002200183700001800205856008700223       2013                          d00aInferring human activities from GPS tracks UrbComp0 aInferring human activities from GPS tracks UrbComp  aChicago USA1 aCintia, Paolo1 aFurletti, Barbara1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/publications/inferring-human-activities-gps-tracks-urbcomp01685nas a2200241   4500008003900000020002200039245006100061210006000122260003800182300001200220520094300232100001901175700002101194700002301215700001801238700002001256700002301276700002301299700002501322700002401347700002101371856005101392       2013                          d  a978-3-319-00614-700aPrivacy-Preserving Distributed Movement Data Aggregation0 aPrivacyPreserving Distributed Movement Data Aggregation  bSpringer International Publishing  a225-2453 aWe propose a novel approach to privacy-preserving analytical processing within a distributed setting, and tackle the problem of obtaining aggregated information about vehicle traffic in a city from movement data collected by individual vehicles and shipped to a central server. Movement data are sensitive because people’s whereabouts have the potential to reveal intimate personal traits, such as religious or sexual preferences, and may allow re-identification of individuals in a database. We provide a privacy-preserving framework for movement data aggregation based on trajectory generalization in a distributed environment. The proposed solution, based on the differential privacy model and on sketching techniques for efficient data compression, provides a formal data protection safeguard. Using real-life data, we demonstrate the effectiveness of our approach also in terms of data utility preserved by the data transformation.1 aMonreale, Anna1 aWang, Hui, Wendy1 aPratesi, Francesca1 aRinzivillo, S1 aPedreschi, Dino1 aAndrienko, Gennady1 aAndrienko, Natalia1 aVandenbroucke, Danny1 aBucher, Bénédicte1 aCrompvoets, Joep  uhttp://dx.doi.org/10.1007/978-3-319-00615-4_1300505nas a2200133   4500008003900000245005400039210005100093100003200144700002300176700003100199700003800230700001800268856008500286       2013                          d00aA Proactive Ap- plication to Monitor Truck Fleets0 aProactive Ap plication to Monitor Truck Fleets1 aAlbuquerque, Fabio Da Costa1 aCasanova, Marco, A1 aCarvalho, Marcelo Tilio, M1 aMacêdo, José Antônio Fernandes1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/publications/proactive-ap-plication-monitor-truck-fleets00526nas a2200133   4500008003900000245008000039210006900119490001400188100002000202700001800222700002100240700002000261856011100281       2013                          d00aSpatial and Temporal Evaluation of Network-based Analysis of Human Mobility0 aSpatial and Temporal Evaluation of Networkbased Analysis of Huma0 vto appear1 aCoscia, Michele1 aRinzivillo, S1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/spatial-and-temporal-evaluation-network-based-analysis-human-mobility00467nas a2200133   4500008003900000245005400039210005300093100002000146700002100166700002000187700001800207700002000225856008800245       2013                          d00aSpatio temporal keyword-queries in Social Networs0 aSpatio temporal keywordqueries in Social Networs1 aCozza, Vittoria1 aMessina, Antonio1 aMontesi, Danilo1 aArietta, Luca1 aMagnani, Matteo  uhttps://kdd.isti.cnr.it/publications/spatio-temporal-keyword-queries-social-networs00453nas a2200145   4500008004100000245004600041210004200087100002400129700002100153700002100174700002000195700002100215700002000236856005100256       2013                        eng d00aThe Three Dimensions of Social Prominence0 aThree Dimensions of Social Prominence1 aPennacchioli, Diego1 aRossetti, Giulio1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca1 aCoscia, Michele  uhttp://dx.doi.org/10.1007/978-3-319-03260-3_2800549nas a2200169   4500008004100000245006800041210006600109300001200175490000700187100001800194700002100212700002700233700002000260700003000280700002200310856004700332       2013                        eng d00aTowards mega-modeling: a walk through data analysis experiences0 aTowards megamodeling a walk through data analysis experiences  a19–270 v421 aCeri, Stefano1 aPalpanas, Themis1 aValle, Emanuele, Della1 aPedreschi, Dino1 aFreytag, Johann-Christoph1 aTrasarti, Roberto  uhttp://doi.acm.org/10.1145/2536669.253667300611nas a2200169   4500008004100000245005200041210005000093260000900143100002200152700002000174700002000194700002100214700002400235700001700259700002000276856014500296       2013                        eng d00aXTribe: a web-based social computation platform0 aXTribe a webbased social computation platform  bIEEE1 aCaminiti, Saverio1 aCicali, Claudio1 aGravino, Pietro1 aLoreto, Vittorio1 aServedio, Vito, D P1 aSirbu, Alina1 aTria, Francesca  uhttp://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6686061&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D668606100579nas a2200133   4500008003900000245009500039210006900134100002000203700002100223700002400244700002400268700002100292856013200313       2013                          d00aYou Know Because I Know”: a Multidimensional Network Approach to Human Resources Problem0 aYou Know Because I Know a Multidimensional Network Approach to H1 aCoscia, Michele1 aRossetti, Giulio1 aPennacchioli, Diego1 aCeccarelli, Damiano1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/you-know-because-i-know%E2%80%9D-multidimensional-network-approach-human-resources-problem00754nas a2200241   4500008004100000245007800041210006900119100001900188700002100207700001700228700002100245700001900266700001600285700002300301700001700324700001700341700002000358700002000378700002200398700002600420700001600446856005000462       2012                        eng d00aAn Agent-Based Model to Evaluate Carpooling at Large Manufacturing Plants0 aAgentBased Model to Evaluate Carpooling at Large Manufacturing P1 aBellemans, Tom1 aBothe, Sebastian1 aCho, Sungjin1 aGiannotti, Fosca1 aJanssens, Davy1 aKnapen, Luk1 aKörner, Christine1 aMay, Michael1 aNanni, Mirco1 aPedreschi, Dino1 aStange, Hendrik1 aTrasarti, Roberto1 aYasar, Ansar-Ul-Haque1 aWets, Geert  uhttp://dx.doi.org/10.1016/j.procs.2012.08.00101793nas a2200145   4500008003900000245007100039210006900110300001400179520131900193100001901512700002001531700001901551700002101570856005601591       2012                          d00aClassifying Trust/Distrust Relationships in Online Social Networks0 aClassifying TrustDistrust Relationships in Online Social Network  a552–5573 aOnline social networks are increasingly being used as places where communities gather to exchange information, form opinions, collaborate in response to events. An aspect of this information exchange is how to determine if a source of social information can be trusted or not. Data mining literature addresses this problem. However, if usually employs social balance theories, by looking at small structures in complex networks known as triangles. This has proven effective in some cases, but it under performs in the lack of context information about the relation and in more complex interactive structures. In this paper we address the problem of creating a framework for the trust inference, able to infer the trust/distrust relationships in those relational environments that cannot be described by using the classical social balance theory. We do so by decomposing a trust network in its ego network components and mining on this ego network set the trust relationships, extending a well known graph mining algorithm. We test our framework on three public datasets describing trust relationships in the real world (from the social media Epinions, Slash dot and Wikipedia) and confronting our results with the trust inference state of the art, showing better performances where the social balance theory fails.1 aBachi, Giacomo1 aCoscia, Michele1 aMonreale, Anna1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1109/SocialCom-PASSAT.2012.11500612nas a2200169   4500008003900000245006900039210006800108260000900176300001300185100001900198700002500217700002200242700001800264700003800282700002300320856009900343       2012                          d00aComeTogether: Discovering Communities of Places in Mobility Data0 aComeTogether Discovering Communities of Places in Mobility Data  c2012  a 268-2731 aBrilhante, Igo1 aBerlingerio, Michele1 aTrasarti, Roberto1 aRenso, Chiara1 aMacêdo, José Antônio Fernandes1 aCasanova, Marco, A  uhttps://kdd.isti.cnr.it/publications/cometogether-discovering-communities-places-mobility-data00430nas a2200121   4500008004100000245007000041210006800111100002000179700002100199700002100220700002000241856004700261       2012                        eng d00aDEMON: a local-first discovery method for overlapping communities0 aDEMON a localfirst discovery method for overlapping communities1 aCoscia, Michele1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttp://doi.acm.org/10.1145/2339530.233963000502nas a2200133   4500008003900000245007000039210006800109260000900177100002000186700002100206700002100227700002000248856010000268       2012                          d00aDEMON: a Local-First Discovery Method for Overlapping Communities0 aDEMON a LocalFirst Discovery Method for Overlapping Communities  c20121 aCoscia, Michele1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/demon-local-first-discovery-method-overlapping-communities01549nas a2200169   4500008003900000022001400039245005900053210005900112520102300171100001801194700002101212700001901233700002001252700002101272700002001293856006601313       2012                          d  a0933-187500aDiscovering the Geographical Borders of Human Mobility0 aDiscovering the Geographical Borders of Human Mobility3 aThe availability of massive network and mobility data from diverse domains has fostered the analysis of human behavior and interactions. Broad, extensive, and multidisciplinary research has been devoted to the extraction of non-trivial knowledge from this novel form of data. We propose a general method to determine the influence of social and mobility behavior over a specific geographical area in order to evaluate to what extent the current administrative borders represent the real basin of human movement. We build a network representation of human movement starting with vehicle GPS tracks and extract relevant clusters, which are then mapped back onto the territory, finding a good match with the existing administrative borders. The novelty of our approach is the focus on a detailed spatial resolution, we map emerging borders in terms of individual municipalities, rather than macro regional or national areas. We present a series of experiments to illustrate and evaluate the effectiveness of our approach.1 aRinzivillo, S1 aMainardi, Simone1 aPezzoni, Fabio1 aCoscia, Michele1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/article/10.1007%2Fs13218-012-0181-801654nas a2200169   4500008004100000022001400041245009000055210006900145260001300214300001100227490000800238520105800246100001701304700002301321700001801344856012201362       2012                        eng d  a1611-753000aIntegrating heterogeneous gene expression data for gene regulatory network modelling.0 aIntegrating heterogeneous gene expression data for gene regulato  c2012 Jun  a95-1020 v1313 a<p>Gene regulatory networks (GRNs) are complex biological systems that have a large impact on protein levels, so that discovering network interactions is a major objective of systems biology. Quantitative GRN models have been inferred, to date, from time series measurements of gene expression, but at small scale, and with limited application to real data. Time series experiments are typically short (number of time points of the order of ten), whereas regulatory networks can be very large (containing hundreds of genes). This creates an under-determination problem, which negatively influences the results of any inferential algorithm. Presented here is an integrative approach to model inference, which has not been previously discussed to the authors' knowledge. Multiple heterogeneous expression time series are used to infer the same model, and results are shown to be more robust to noise and parameter perturbation. Additionally, a wavelet analysis shows that these models display limited noise over-fitting within the individual datasets.</p>1 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin  uhttps://kdd.isti.cnr.it/publications/integrating-heterogeneous-gene-expression-data-gene-regulatory-network-modelling00381nas a2200121   4500008004100000245004100041210004000082100001800122700002700140700002000167700002200187856005000209       2012                        eng d00aMega-modeling for Big Data Analytics0 aMegamodeling for Big Data Analytics1 aCeri, Stefano1 aValle, Emanuele, Della1 aPedreschi, Dino1 aTrasarti, Roberto  uhttp://dx.doi.org/10.1007/978-3-642-34002-4_102055nas a2200169   4500008003900000245006600039210006500105260001200170490002200182520150900204100002501713700002001738700002101758700001901779700002001798856006701818       2012                          d00aMultidimensional networks: foundations of structural analysis0 aMultidimensional networks foundations of structural analysis  c10/20120 v Volume 15 / 20123 aComplex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. So far, network analysis has focused on the characterization and measurement of local and global properties of graphs, such as diameter, degree distribution, centrality, and so on. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens in monodimensional networks, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we present a solid repertoire of basic concepts and analytical measures, which take into account the general structure of multidimensional networks. We tested our framework on different real world multidimensional networks, showing the validity and the meaningfulness of the measures introduced, that are able to extract important and non-random information about complex phenomena in such networks. 1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttp://www.springerlink.com/content/f774289854430410/abstract/00498nas a2200133   4500008003900000245006600039210006600105260002200171100002000193700001800213700002000231700002100251856009200272       2012                          d00aOptimal Spatial Resolution for the Analysis of Human Mobility0 aOptimal Spatial Resolution for the Analysis of Human Mobility  aInstanbul, Turkey1 aCoscia, Michele1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/optimal-spatial-resolution-analysis-human-mobility02529nas a2200181   4500008004100000022001400041245012200055210006900177260000900246300001100255490000600266520187200272100001702144700001902161700001802180700002302198856012602221       2012                        eng d  a1932-620300aRNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering.0 aRNASeq vs dual and singlechannel microarray data sensitivity ana  c2012  ae509860 v73 a<p>With the fast development of high-throughput sequencing technologies, a new generation of genome-wide gene expression measurements is under way. This is based on mRNA sequencing (RNA-seq), which complements the already mature technology of microarrays, and is expected to overcome some of the latter's disadvantages. These RNA-seq data pose new challenges, however, as strengths and weaknesses have yet to be fully identified. Ideally, Next (or Second) Generation Sequencing measures can be integrated for more comprehensive gene expression investigation to facilitate analysis of whole regulatory networks. At present, however, the nature of these data is not very well understood. In this paper we study three alternative gene expression time series datasets for the Drosophila melanogaster embryo development, in order to compare three measurement techniques: RNA-seq, single-channel and dual-channel microarrays. The aim is to study the state of the art for the three technologies, with a view of assessing overlapping features, data compatibility and integration potential, in the context of time series measurements. This involves using established tools for each of the three different technologies, and technical and biological replicates (for RNA-seq and microarrays, respectively), due to the limited availability of biological RNA-seq replicates for time series data. The approach consists of a sensitivity analysis for differential expression and clustering. In general, the RNA-seq dataset displayed highest sensitivity to differential expression. The single-channel data performed similarly for the differentially expressed genes common to gene sets considered. Cluster analysis was used to identify different features of the gene space for the three datasets, with higher similarities found for the RNA-seq and single-channel microarray dataset.</p>1 aSirbu, Alina1 aKerr, Gráinne1 aCrane, Martin1 aRuskin, Heather, J  uhttps://kdd.isti.cnr.it/publications/rna-seq-vs-dual-and-single-channel-microarray-data-sensitivity-analysis-differential02085nas a2200805   4500008004100000245005500041210005500096300001200151490000600163100002000169700001900189700002100208700001500229700001600244700001800260700001800278700002000296700001800316700001700334700002500351700001600376700001300392700001600405700001800421700002000439700001400459700001700473700001500490700002100505700001800526700001500544700001900559700002000578700001700598700001500615700001600630700001700646700002000663700001600683700001800699700001400717700001500731700001400746700001300760700001700773700001500790700001500805700001500820700001400835700001400849700001900863700001400882700002300896700001400919700001500933700001700948700001300965700001600978700001800994700001501012700002001027700001601047700001701063700001801080700001701098700001801115700001401133700001501147856011701162       2012                        eng d00aWisdom of crowds for robust gene network inference0 aWisdom of crowds for robust gene network inference  a796-8040 v91 aMarbach, Daniel1 aCostello, J.C.1 aKüffner, Robert1 aVega, N.M.1 aPrill, R.J.1 aCamacho, D.M.1 aAllison, K.R.1 aKellis, Manolis1 aCollins, J.J.1 aAderhold, A.1 aStolovitzky, Gustavo1 aBonneau, R.1 aChen, Y.1 aCordero, F.1 aCrane, Martin1 aDondelinger, F.1 aDrton, M.1 aEsposito, R.1 aFoygel, R.1 aDe La Fuente, A.1 aGertheiss, J.1 aGeurts, P.1 aGreenfield, A.1 aGrzegorczyk, M.1 aHaury, A.-C.1 aHolmes, B.1 aHothorn, T.1 aHusmeier, D.1 aHuynh-Thu, V.A.1 aIrrthum, A.1 aKarlebach, G.1 aLebre, S.1 aDe Leo, V.1 aMadar, A.1 aMani, S.1 aMordelet, F.1 aOstrer, H.1 aOuyang, Z.1 aPandya, R.1 aPetri, T.1 aPinna, A.1 aPoultney, C.S.1 aRezny, S.1 aRuskin, Heather, J1 aSaeys, Y.1 aShamir, R.1 aSirbu, Alina1 aSong, M.1 aSoranzo, N.1 aStatnikov, A.1 aVega, N.M.1 aVera-Licona, P.1 aVert, J.-P.1 aVisconti, A.1 aWang, Haizhou1 aWehenkel, L.1 aWindhager, L.1 aZhang, Y.1 aZimmer, R.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84870305264&partnerID=40&md5=04a686572bdefff60157bf68c95df7ea01936nas a2200265   4500008004100000022001400041245005600055210005500111260001300166300001200179490000600191520117600197100002001373700001901393700002101412700001501433700001601448700001801464700001801482700002001500700001801520700002501538710002201563856008501585       2012                        eng d  a1548-710500aWisdom of crowds for robust gene network inference.0 aWisdom of crowds for robust gene network inference  c2012 Aug  a796-8040 v93 a<p>Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.</p>1 aMarbach, Daniel1 aCostello, J.C.1 aKüffner, Robert1 aVega, N.M.1 aPrill, R.J.1 aCamacho, D.M.1 aAllison, K.R.1 aKellis, Manolis1 aCollins, J.J.1 aStolovitzky, Gustavo1 aDREAM5 Consortium  uhttps://kdd.isti.cnr.it/publications/wisdom-crowds-robust-gene-network-inference00495nas a2200133   4500008003900000245007300039210006900112300001200181490000600193100002000199700002100219700002000240856010100260       2011                          d00aA classification for community discovery methods in complex networks0 aclassification for community discovery methods in complex networ  a512-5460 v41 aCoscia, Michele1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/classification-community-discovery-methods-complex-networks00486nas a2200121   4500008003900000245007200039210006900111300001200180100002500192700002000217700002100237856010600258       2011                          d00aFinding and Characterizing Communities in Multidimensional Networks0 aFinding and Characterizing Communities in Multidimensional Netwo  a490-4941 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/finding-and-characterizing-communities-multidimensional-networks00506nas a2200121   4500008003900000245008100039210006900120300001400189100002500203700002000228700002100248856011500269       2011                          d00aFinding redundant and complementary communities in multidimensional networks0 aFinding redundant and complementary communities in multidimensio  a2181-21841 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/finding-redundant-and-complementary-communities-multidimensional-networks01804nas a2200157   4500008003900000245005300039210005300092300001200145520129700157100002501454700002001479700002101499700001901520700002001539856008701559       2011                          d00aFoundations of Multidimensional Network Analysis0 aFoundations of Multidimensional Network Analysis  a485-4893 aComplex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens inmonodimensional network, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we develop a solid repertoire of basic concepts and analytical measures, which takes into account the general structure of multidimensional networks. We tested our framework on a real world multidimensional network, showing the validity and the meaningfulness of the measures introduced, that are able to extract important, nonrandom, information about complex phenomena.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/foundations-multidimensional-network-analysis01999nas a2200169   4500008003900000245008200039210006900121300001200190490000600202520141100208100002501619700002001644700002101664700001901685700002001704856010501724       2011                          d00aThe pursuit of hubbiness: Analysis of hubs in large multidimensional networks0 apursuit of hubbiness Analysis of hubs in large multidimensional   a223-2370 v23 aHubs are highly connected nodes within a network. In complex network analysis, hubs have been widely studied, and are at the basis of many tasks, such as web search and epidemic outbreak detection. In reality, networks are often multidimensional, i.e., there can exist multiple connections between any pair of nodes. In this setting, the concept of hub depends on the multiple dimensions of the network, whose interplay becomes crucial for the connectedness of a node. In this paper, we characterize multidimensional hubs. We consider the multidimensional generalization of the degree and introduce a new class of measures, that we call Dimension Relevance, aimed at analyzing the importance of different dimensions for the hubbiness of a node. We assess the meaningfulness of our measures by comparing them on real networks and null models, then we study the interplay among dimensions and their effect on node connectivity. Our findings show that: (i) multidimensional hubs do exist and their characterization yields interesting insights and (ii) it is possible to detect the most influential dimensions that cause the different hub behaviors. We demonstrate the usefulness of multidimensional analysis in three real world domains: detection of ambiguous query terms in a word–word query log network, outlier detection in a social network, and temporal analysis of behaviors in a co-authorship network.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/pursuit-hubbiness-analysis-hubs-large-multidimensional-networks00508nas a2200121   4500008004100000245008100041210006900122260001100191100001700202700002300219700001800242856012600260       2011                        eng d00aStages of Gene Regulatory Network Inference: the Evolutionary Algorithm Role0 aStages of Gene Regulatory Network Inference the Evolutionary Alg  bInTech1 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin  uhttp://www.intechopen.com/articles/show/title/stages-of-gene-regulatory-network-inference-the-evolutionary-algorithm-role01803nas a2200157   4500008003900000245006600039210006500105300001000170520126700180100002501447700002001472700002101492700001901513700002001532856009301552       2010                          d00aAs Time Goes by: Discovering Eras in Evolving Social Networks0 aAs Time Goes by Discovering Eras in Evolving Social Networks  a81-903 aWithin the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolution on the global and the local scale, extracting properties of either the entire network or local patterns. In this paper, we focus instead on detecting clusters of temporal snapshots of a network, to be interpreted as eras of evolution. To this aim, we introduce a novel hierarchical clustering methodology, based on a dissimilarity measure (derived from the Jaccard coefficient) between two temporal snapshots of the network. We devise a framework to discover and browse the eras, either in top-down or a bottom-up fashion, supporting the exploration of the evolution at any level of temporal resolution. We show how our approach applies to real networks, by detecting eras in an evolving co-authorship graph extracted from a bibliographic dataset; we illustrate how the discovered temporal clustering highlights the crucial moments when the network had profound changes in its structure. Our approach is finally boosted by introducing a meaningful labeling of the obtained clusters, such as the characterizing topics of each discovered era, thus adding a semantic dimension to our analysis.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/time-goes-discovering-eras-evolving-social-networks01924nas a2200169   4500008004100000022001400041245008600055210006900141260000900210300000700219490000700226520134700233100001701580700002301597700001801620856011601638       2010                        eng d  a1471-210500aComparison of evolutionary algorithms in gene regulatory network model inference.0 aComparison of evolutionary algorithms in gene regulatory network  c2010  a590 v113 a<p><b>BACKGROUND: </b>The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient.</p><p><b>RESULTS: </b>This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared.</p><p><b>CONCLUSIONS: </b>Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.</p>1 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin  uhttps://kdd.isti.cnr.it/publications/comparison-evolutionary-algorithms-gene-regulatory-network-model-inference01557nas a2200169   4500008004100000022001400041245008300055210006900138260000900207300001100216490000600227520098100233100001701214700002301231700001801254856011501272       2010                        eng d  a1932-620300aCross-platform microarray data normalisation for regulatory network inference.0 aCrossplatform microarray data normalisation for regulatory netwo  c2010  ae138220 v53 a<p><b>BACKGROUND: </b>Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences.</p><p><b>METHODS: </b>We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets.</p><p><b>CONCLUSIONS: </b>Results identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem.</p>1 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin  uhttps://kdd.isti.cnr.it/publications/cross-platform-microarray-data-normalisation-regulatory-network-inference00537nas a2200145   4500008003900000245006900039210006700108300001000175100002500185700002000210700002100230700001900251700002000270856010100290       2010                          d00aDiscovering Eras in Evolving Social Networks (Extended Abstract)0 aDiscovering Eras in Evolving Social Networks Extended Abstract  a78-851 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/discovering-eras-evolving-social-networks-extended-abstract00450nas a2200121   4500008004100000245008700041210006900128300001600197100001700213700002300230700001800253856005700271       2010                        eng d00aRegulatory network modelling: Correlation for structure and parameter optimisation0 aRegulatory network modelling Correlation for structure and param  a3473–34811 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin  uhttp://www.actapress.com/Abstract.aspx?paperId=4157301363nas a2200145   4500008003900000245004900039210004900088520089500137100002501032700002001057700002101077700001901098700002001117856008001137       2010                          d00aTowards Discovery of Eras in Social Networks0 aTowards Discovery of Eras in Social Networks3 aIn the last decades, much research has been devoted in topics related to Social Network Analysis. One important direction in this area is to analyze the temporal evolution of a network. So far, previous approaches analyzed this setting at both the global and the local level. In this paper, we focus on finding a way to detect temporal eras in an evolving network. We pose the basis for a general framework that aims at helping the analyst in browsing the temporal clusters both in a top-down and bottom-up way, exploring the network at any level of temporal details. We show the effectiveness of our approach on real data, by applying our proposed methodology to a co-authorship network extracted from a bibliographic dataset. Our first results are encouraging, and open the way for the definition and implementation of a general framework for discovering eras in evolving social networks.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/towards-discovery-eras-social-networks00573nas a2200145   4500008003900000245008400039210006900123300001200192100002500204700002200229700002000251700002100271700001900292856011600311       2009                          d00aMining Clinical, Immunological, and Genetic Data of Solid Organ Transplantation0 aMining Clinical Immunological and Genetic Data of Solid Organ Tr  a211-2361 aBerlingerio, Michele1 aBonchi, Francesco1 aCurcio, Michele1 aGiannotti, Fosca1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/mining-clinical-immunological-and-genetic-data-solid-organ-transplantation00425nas a2200121   4500008003900000245005200039210005200091300001200143100002500155700002000180700002100200856008200221       2009                          d00aMining the Information Propagation in a Network0 aMining the Information Propagation in a Network  a333-3401 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mining-information-propagation-network-000423nas a2200121   4500008003900000245005200039210005200091300001200143100002500155700002000180700002100200856008000221       2009                          d00aMining the Information Propagation in a Network0 aMining the Information Propagation in a Network  a333-3401 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mining-information-propagation-network00455nas a2200121   4500008003900000245006500039210006500104300001200169100002500181700002000206700002100226856008600247       2009                          d00aMining the Temporal Dimension of the Information Propagation0 aMining the Temporal Dimension of the Information Propagation  a237-2481 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/mining-temporal-dimension-information-propagation00460nas a2200121   4500008003900000245006500039210006500104300001200169100002500181700002000206700002100226856009100247       2009                          d00aMining the Temporal Dimension of the Information Propagation0 aMining the Temporal Dimension of the Information Propagation  a237-2481 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mining-temporal-dimension-information-propagation00527nas a2200121   4500008003900000245009700039210006900136300001200205100002000217700002100237700002200258856012500280       2009                          d00aSocial Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography0 aSocial Network Analysis as Knowledge Discovery Process A Case St  a279-2831 aCoscia, Michele1 aGiannotti, Fosca1 aPensa, Ruggero, G  uhttps://kdd.isti.cnr.it/publications/social-network-analysis-knowledge-discovery-process-case-study-digital-bibliography00529nas a2200121   4500008003900000245009700039210006900136300001200205100002000217700002100237700002200258856012700280       2009                          d00aSocial Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography0 aSocial Network Analysis as Knowledge Discovery Process A Case St  a279-2831 aCoscia, Michele1 aGiannotti, Fosca1 aPensa, Ruggero, G  uhttps://kdd.isti.cnr.it/publications/social-network-analysis-knowledge-discovery-process-case-study-digital-bibliography-000698nas a2200205   4500008004100000245008100041210006900122300001000191490000700201100002600208700001800234700001500252700002100267700001500288700002200303700001800325700002600343700001900369856010400388       2008                        eng d00aAn Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology0 aApplication of Advanced SpatioTemporal Formalisms to Behavioural  a37-720 v121 aRaffaetà, Alessandra1 aCeccarelli, T1 aCenteno, D1 aGiannotti, Fosca1 aMassolo, A1 aParent, Christine1 aRenso, Chiara1 aSpaccapietra, Stefano1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/application-advanced-spatio-temporal-formalisms-behavioural-ecology00659nas a2200181   4500008004100000245008100041210006900122100001800191700001500209700002100224700001500245700002200260700002600282700001800308700002600326700001900352856010600371       2008                        eng d00aAn Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology0 aApplication of Advanced SpatioTemporal Formalisms to Behavioural1 aCeccarelli, T1 aCenteno, D1 aGiannotti, Fosca1 aMassolo, A1 aParent, Christine1 aRaffaetà, Alessandra1 aRenso, Chiara1 aSpaccapietra, Stefano1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/application-advanced-spatio-temporal-formalisms-behavioural-ecology-000662nas a2200193   4500008004100000245007300041210006900114300001200183100002100195700001500216700001600231700002200247700001800269700002100287700002000308700001800328700002400346856009800370       2008                        eng d00aDAEDALUS: A knowledge discovery analysis framework for movement data0 aDAEDALUS A knowledge discovery analysis framework for movement d  a191-1981 aOrtale, Riccardo1 aRitacco, E1 aPelekisy, N1 aTrasarti, Roberto1 aCosta, Gianni1 aGiannotti, Fosca1 aManco, Giuseppe1 aRenso, Chiara1 aTheodoridis, Yannis  uhttps://kdd.isti.cnr.it/content/daedalus-knowledge-discovery-analysis-framework-movement-data00665nas a2200193   4500008003900000245007700039210006900116300000700185100002100192700001500213700001900228700002200247700001800269700002100287700002000308700001800328700002400346856010100370       2008                          d00aThe DAEDALUS framework: progressive querying and mining of movement data0 aDAEDALUS framework progressive querying and mining of movement d  a521 aOrtale, Riccardo1 aRitacco, E1 aPelekis, Nikos1 aTrasarti, Roberto1 aCosta, Gianni1 aGiannotti, Fosca1 aManco, Giuseppe1 aRenso, Chiara1 aTheodoridis, Yannis  uhttps://kdd.isti.cnr.it/content/daedalus-framework-progressive-querying-and-mining-movement-data00524nas a2200157   4500008004100000245005000041210005000091300001200141100002500153700002200178700002100200700002000221700002100241700002200262856008200284       2006                        eng d00aMining HLA Patterns Explaining Liver Diseases0 aMining HLA Patterns Explaining Liver Diseases  a702-7071 aBerlingerio, Michele1 aBonchi, Francesco1 aChelazzi, Silvia1 aCurcio, Michele1 aGiannotti, Fosca1 aScatena, Fabrizio  uhttps://kdd.isti.cnr.it/content/mining-hla-patterns-explaining-liver-diseases00584nas a2200133   4500008004100000245011800041210006900159300001000228100002500238700002100263700002100284700002500305856012000330       1996                        eng d00aRagionamento spazio-temporale con LDLT: primi esperimenti verso un sistema deduttivo per applicazioni geografiche0 aRagionamento spaziotemporale con LDLT primi esperimenti verso un  a73-901 aCarboni, Marilisa, E1 aDeo, Annalisa Di1 aGiannotti, Fosca1 aMasserotti, Maria, V  uhttps://kdd.isti.cnr.it/content/ragionamento-spazio-temporale-con-ldlt-primi-esperimenti-verso-un-sistema-deduttivo00575nas a2200133   4500008004100000245011000041210006900151300001200220100002500232700002100257700002100278700002500299856011700324       1996                        eng d00aSpatio-Temporal Reasoning with LDLT: First Steps Towards a Deductive System for Geographical Applications0 aSpatioTemporal Reasoning with LDLT First Steps Towards a Deducti  a135-1511 aCarboni, Marilisa, E1 aDeo, Annalisa Di1 aGiannotti, Fosca1 aMasserotti, Maria, V  uhttps://kdd.isti.cnr.it/content/spatio-temporal-reasoning-ldlt-first-steps-towards-deductive-system-geographical00526nas a2200133   4500008004100000245008500041210006900126300000900195100002500204700002100229700001400250700002000264856010800284       1995                        eng d00aDeclarative Reconstruction of Updates in Logic Databases: A Compilative Approach0 aDeclarative Reconstruction of Updates in Logic Databases A Compi  a3-131 aCarboni, Marilisa, E1 aGiannotti, Fosca1 aFoddai, V1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/declarative-reconstruction-updates-logic-databases-compilative-approach00531nas a2200133   4500008004100000245008500041210006900126300001200195100002500207700001400232700002100246700002000267856011000287       1995                        eng d00aDeclarative Reconstruction of Updates in Logic Databases: a Compilative Approach0 aDeclarative Reconstruction of Updates in Logic Databases a Compi  a169-1821 aCarboni, Marilisa, E1 aFoddai, V1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/declarative-reconstruction-updates-logic-databases-compilative-approach-000514nas a2200133   4500008004100000245007800041210006900119300001000188100001900198700002100217700002000238700001900258856010300277       1994                        eng d00aExpressive Power of Non-Deterministic Operators for Logic-based Languages0 aExpressive Power of NonDeterministic Operators for Logicbased La  a27-401 aCorciulo, Luca1 aGiannotti, Fosca1 aPedreschi, Dino1 aZaniolo, Carlo  uhttps://kdd.isti.cnr.it/content/expressive-power-non-deterministic-operators-logic-based-languages00549nas a2200169   4500008004100000245005400041210005400095300001400149100001900163700001700182700002200199700001600221700002000237700001800257700001900275856008500294       1994                        eng d00aImplementations of Program Composition Operations0 aImplementations of Program Composition Operations  a292–3071 aBrogi, Antonio1 aChiarelli, A1 aMancarella, Paolo1 aMazzotta, V1 aPedreschi, Dino1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/implementations-program-composition-operations-100545nas a2200169   4500008004100000245005400041210005400095300001200149100001900161700001700180700002200197700001600219700002000235700001800255700001900273856008300292       1994                        eng d00aImplementations of Program Composition Operations0 aImplementations of Program Composition Operations  a292-3071 aBrogi, Antonio1 aChiarelli, A1 aMancarella, Paolo1 aMazzotta, V1 aPedreschi, Dino1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/implementations-program-composition-operations00547nas a2200169   4500008004100000245005400041210005400095300001200149100001900161700001700180700002200197700001600219700002000235700001800255700001900273856008500292       1994                        eng d00aImplementations of Program Composition Operations0 aImplementations of Program Composition Operations  a292-3071 aBrogi, Antonio1 aChiarelli, A1 aMancarella, Paolo1 aMazzotta, V1 aPedreschi, Dino1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/implementations-program-composition-operations-000441nas a2200121   4500008004100000245006100041210005900102300001000161100001900171700002100190700002000211856008800231       1993                        eng d00aDatalog with Non-Deterministic Choice Computes NDB-PTIME0 aDatalog with NonDeterministic Choice Computes NDBPTIME  a49-661 aCorciulo, Luca1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/datalog-non-deterministic-choice-computes-ndb-ptime00418nas a2200121   4500008004100000245005600041210005400097300001200151100001700163700001600180700001800196856008200214       1993                        eng d00aA WAM Estesa per la Composizione di Programi Logici0 aWAM Estesa per la Composizione di Programi Logici  a189-2021 aChiarelli, A1 aMazzotta, V1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/content/wam-estesa-la-composizione-di-programi-logici