00485nas a2200109   4500008004100000245008900041210006900130100002600199700002100225700001900246856011000265       2022                        eng d00aFrom Mean-Field to Complex Topologies: Network Effects on the Algorithmic Bias Model0 aFrom MeanField to Complex Topologies Network Effects on the Algo1 aPansanella, Valentina1 aRossetti, Giulio1 aMilli, Letizia  uhttps://kdd.isti.cnr.it/publications/mean-field-complex-topologies-network-effects-algorithmic-bias-model01450nas 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/932134800465nas 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-networks01328nas a2200133   4500008004100000245005300041210005300094260001300147520090400160100002301064700001901087700002101106856006701127       2020                        eng d00aOpinion Dynamic Modeling of Fake News Perception0 aOpinion Dynamic Modeling of Fake News Perception  bSpringer3 aFake news diffusion represents one of the most pressing issues of our online society. In recent years, fake news has been analyzed from several points of view, primarily to improve our ability to separate them from the legit ones as well as identify their sources. Among such vast literature, a rarely discussed theme is likely to play uttermost importance in our understanding of such a controversial phenomenon: the analysis of fake news’ perception. In this work, we approach such a problem by proposing a family of opinion dynamic models tailored to study how specific social interaction patterns concur to the acceptance, or refusal, of fake news by a population of interacting individuals. To discuss the peculiarities of the proposed models, we tested them on several synthetic network topologies, thus underlying when/how they affect the stable states reached by the performed simulations.1 aToccaceli, Cecilia1 aMilli, Letizia1 aRossetti, Giulio  uhttps://link.springer.com/chapter/10.1007/978-3-030-65347-7_3100533nas 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-interventions01289nas 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/s41109-019-0165-900442nas a2200109   4500008004100000245006800041210006600109260001300175100001900188700002100207856010400228       2019                        eng d00aCommunity-Aware Content Diffusion: Embeddednes and Permeability0 aCommunityAware Content Diffusion Embeddednes and Permeability  bSpringer1 aMilli, Letizia1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/community-aware-content-diffusion-embeddednes-and-permeability01882nas a2200157   4500008004100000245006300041210006300104300000700167490000600174520139900180100001901579700002101598700002001619700002101639856006401660       2018                        eng d00aActive and passive diffusion processes in complex networks0 aActive and passive diffusion processes in complex networks  a420 v33 aIdeas, information, viruses: all of them, with their mechanisms, spread over the complex social information, viruses: all tissues described by our interpersonal relations. Usually, to simulate and understand the unfolding of such complex phenomena are used general mathematical models; these models act agnostically from the object of which they simulate the diffusion, thus considering spreading of virus, ideas and innovations alike. Indeed, such degree of abstraction makes it easier to define a standard set of tools that can be applied to heterogeneous contexts; however, it can also lead to biased, incorrect, simulation outcomes. In this work we introduce the concepts of active and passive diffusion to discriminate the degree in which individuals choice affect the overall spreading of content over a social graph. Moving from the analysis of a well-known passive diffusion schema, the Threshold model (that can be used to model peer-pressure related processes), we introduce two novel approaches whose aim is to provide active and mixed schemas applicable in the context of innovations/ideas diffusion simulation.

Our analysis, performed both in synthetic and real-world data, underline that the adoption of exclusively passive/active models leads to conflicting results, thus highlighting the need of mixed approaches to capture the real complexity of the simulated system better.1 aMilli, Letizia1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://link.springer.com/article/10.1007/s41109-018-0100-501271nas a2200145   4500008004100000245006500041210006300106260003600169520077200205100001900977700002100996700002001017700002101037856006701058       2018                        eng d00aDiffusive Phenomena in Dynamic Networks: a data-driven study0 aDiffusive Phenomena in Dynamic Networks a datadriven study  aBoston March 5-8 2018bSpringer3 aEveryday, ideas, information as well as viruses spread over complex social tissues described by our interpersonal relations. So far, the network contexts upon which diffusive phenomena unfold have usually considered static, composed by a fixed set of nodes and edges. Recent studies describe social networks as rapidly changing topologies. In this work – following a data-driven approach – we compare the behaviors of classical spreading models when used to analyze a given social network whose topological dynamics are observed at different temporal-granularities. Our goal is to shed some light on the impacts that the adoption of a static topology has on spreading simulations as well as to provide an alternative formulation of two classical diffusion models.1 aMilli, Letizia1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://link.springer.com/chapter/10.1007/978-3-319-73198-8_1301236nas a2200181   4500008004100000245009100041210006900132300001200201490000600213520065500219100002100874700001900895700001800914700001700932700002000949700002100969856006400990       2018                        eng d00aNDlib: a python library to model and analyze diffusion processes over complex networks0 aNDlib a python library to model and analyze diffusion processes   a61–790 v53 aNowadays the analysis of dynamics of and on networks represents a hot topic in the social network analysis playground. To support students, teachers, developers and researchers, in this work we introduce a novel framework, namely NDlib, an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. For this reason, upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians.1 aRossetti, Giulio1 aMilli, Letizia1 aRinzivillo, S1 aSirbu, Alina1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://link.springer.com/article/10.1007/s41060-017-0086-601579nas a2200157   4500008004100000245007400041210006900115300001300184490000700197520103200204100002101236700001901257700002101276700002001297856010401317       2017                        eng d00aForecasting success via early adoptions analysis: A data-driven study0 aForecasting success via early adoptions analysis A datadriven st  ae01890960 v123 aInnovations are continuously launched over markets, such as new products over the retail market or new artists over the music scene. Some innovations become a success; others don’t. Forecasting which innovations will succeed at the beginning of their lifecycle is hard. In this paper, we provide a data-driven, large-scale account of the existence of a special niche among early adopters, individuals that consistently tend to adopt successful innovations before they reach success: we will call them Hit-Savvy. Hit-Savvy can be discovered in very different markets and retain over time their ability to anticipate the success of innovations. As our second contribution, we devise a predictive analytical process, exploiting Hit-Savvy as signals, which achieves high accuracy in the early-stage prediction of successful innovations, far beyond the reach of state-of-the-art time series forecasting models. Indeed, our findings and predictive model can be fruitfully used to support marketing strategies and product placement.1 aRossetti, Giulio1 aMilli, Letizia1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/forecasting-success-early-adoptions-analysis-data-driven-study01474nas a2200145   4500008004100000245007600041210006900117260001300186520098100199100001901180700002101199700002001220700002101240856006701261       2017                        eng d00aInformation diffusion in complex networks: The active/passive conundrum0 aInformation diffusion in complex networks The activepassive conu  bSpringer3 aIdeas, information, viruses: all of them, with their mechanisms, can spread over the complex social tissues described by our interpersonal relations. Classical spreading models can agnostically from the object of which they simulate the diffusion, thus considering spreading of virus, ideas and innovations alike. Indeed, such simplification makes easier to define a standard set of tools that can be applied to heterogeneous contexts; however, it can also lead to biased, partial, simulation outcomes. In this work we discuss the concepts of active and passive diffusion: moving from analysis of a well-known passive model, the Threshold one, we introduce two novel approaches whose aim is to provide active and mixed schemas applicable in the context of innovations/ideas diffusion simulation. Our data-driven analysis shows how, in such context, the adoption of exclusively passive/active models leads to conflicting results, thus highlighting the need of mixed approaches.1 aMilli, Letizia1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://link.springer.com/chapter/10.1007/978-3-319-72150-7_2501274nas a2200169   4500008004100000245009100041210006900132300001100201520065400212100002100866700001900887700001800906700001700924700002000941700002100961856012200982       2017                        eng d00aNDlib: a python library to model and analyze diffusion processes over complex networks0 aNDlib a python library to model and analyze diffusion processes   a1–193 aNowadays the analysis of dynamics of and on networks represents a hot topic in the social network analysis playground.To support students, teachers, developers and researchers, in this work we introduce a novel framework, namely NDlib, an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. For this reason, upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians.1 aRossetti, Giulio1 aMilli, Letizia1 aRinzivillo, S1 aSirbu, Alina1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/ndlib-python-library-model-and-analyze-diffusion-processes-over-complex-networks01119nas a2200169   4500008004100000245004700041210004600088260001000134520063100144100002100775700001900796700001800815700001700833700002000850700002100870856005800891       2017                        eng d00aNDlib: Studying Network Diffusion Dynamics0 aNDlib Studying Network Diffusion Dynamics  aTokyo3 aNowadays the analysis of diffusive phenomena occurring on top of complex networks represents a hot topic in the Social Network Analysis playground. In order to support students, teachers, developers and researchers in this work we introduce a novel simulation framework, ND LIB . ND LIB is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. Upon the diffusion library, we designed a simulation server that allows remote execution of experiments and an online visualization tool that abstract the programmatic interface and makes available the simulation platform to non-technicians.1 aRossetti, Giulio1 aMilli, Letizia1 aRinzivillo, S1 aSirbu, Alina1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://ieeexplore.ieee.org/abstract/document/825977401921nas a2200169   4500008004100000245003800041210003800079260002400117520140400141100001901545700001901564700002101583700002001604700002101624700002501645856008101670       2015                        eng d00aQuantification in Social Networks0 aQuantification in Social Networks  aParis, FrancebIEEE3 aIn many real-world applications there is a need to monitor the distribution of a population across different classes, and to track changes in this distribution over time. As an example, an important task is to monitor the percentage of unemployed adults in a given region. When the membership of an individual in a class cannot be established deterministically, a typical solution is the classification task. However, in the above applications the final goal is not determining which class the individuals belong to, but estimating the prevalence of each class in the unlabeled data. This task is called quantification. Most of the work in the literature addressed the quantification problem considering data presented in conventional attribute format. Since the ever-growing availability of web and social media we have a flourish of network data representing a new important source of information and by using quantification network techniques we could quantify collective behavior, i.e., the number of users that are involved in certain type of activities, preferences, or behaviors. In this paper we exploit the homophily effect observed in many social networks in order to construct a quantifier for networked data. Our experiments show the effectiveness of the proposed approaches and the comparison with the existing state-of-the-art quantification methods shows that they are more accurate. 1 aMilli, Letizia1 aMonreale, Anna1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca1 aSebastiani, Fabrizio  uhttp://www.giuliorossetti.net/about/wp-content/uploads/2015/12/main_DSAA.pdf02456nas a2200205   4500008003900000020002200039245014900061210006900210260002300279520172700302100002202029700002302051700002102074700001902095700001702114700002002131700001902151700002302170856005702193       2014                          d  a978-88-8467-874-400aUse of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach0 aUse of mobile phone data to estimate mobility flows Measuring ur  aCagliari c06/20143 aThe Big Data, originating from the digital breadcrumbs of human activi-
ties, sensed as a by-product of the technologies that we use for our daily activities, let
us to observe the individual and collective behavior of people at an unprecedented
detail. Many dimensions of our social life have big data “proxies”, as the mobile
calls data for mobility. In this paper we investigate to what extent such ”big data”,
in integration with administrative ones, could be a support in producing reliable and
timely estimates of inter-city mobility. The study has been jointly developed by Is-
tat, CNR, University of Pisa in the range of interest of the “Commssione di studio
avente il compito di orientare le scelte dellIstat sul tema dei Big Data ”. In an on-
going project at ISTAT, called “Persons and Places” – based on an integration of
administrative data sources, it has been produced a first release of Origin Destina-
tion matrix – at municipality level – assuming that the places of residence and that
of work (or study) be the terminal points of usual individual mobility for work or
study. The coincidence between the city of residence and that of work (or study) –
is considered as a proxy of the absence of intercity mobility for a person (we define
him a static resident). The opposite case is considered as a proxy of presence of mo-
bility (the person is a dynamic resident: commuter or embedded). As administrative
data do not contain information on frequency of the mobility, the idea is to specify
an estimate method, using calling data as support, to define for each municipality the
stock of standing residents, embedded city users and daily city users (commuters)1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aGiannotti, Fosca1 aMilli, Letizia1 aNanni, Mirco1 aPedreschi, Dino1 aVivio, Roberta1 aGarofalo, Giuseppe  uhttp://www.sis2014.it/proceedings/allpapers/3026.pdf02131nas a2200169   4500008003900000245002500039210002500064300001400089520168900103100001901792700001901811700002101830700002101851700002001872700002501892856004401917       2013                          d00aQuantification Trees0 aQuantification Trees  a528–5363 aIn many applications there is a need to monitor how a population is distributed across different classes, and to track the changes in this distribution that derive from varying circumstances, an example such application is monitoring the percentage (or "prevalence") of unemployed people in a given region, or in a given age range, or at different time periods. When the membership of an individual in a class cannot be established deterministically, this monitoring activity requires classification. However, in the above applications the final goal is not determining which class each individual belongs to, but simply estimating the prevalence of each class in the unlabeled data. This task is called quantification. In a supervised learning framework we may estimate the distribution across the classes in a test set from a training set of labeled individuals. However, this may be sub optimal, since the distribution in the test set may be substantially different from that in the training set (a phenomenon called distribution drift). So far, quantification has mostly been addressed by learning a classifier optimized for individual classification and later adjusting the distribution it computes to compensate for its tendency to either under-or over-estimate the prevalence of the class. In this paper we propose instead to use a type of decision trees (quantification trees) optimized not for individual classification, but directly for quantification. Our experiments show that quantification trees are more accurate than existing state-of-the-art quantification methods, while retaining at the same time the simplicity and understandability of the decision tree framework.1 aMilli, Letizia1 aMonreale, Anna1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino1 aSebastiani, Fabrizio  uhttp://dx.doi.org/10.1109/ICDM.2013.122