@article {1556,
	title = {Attributed Stream Hypergraphs: temporal modeling of node-attributed high-order interactions},
	journal = {Applied Network Science},
	volume = {8},
	number = {1},
	year = {2023},
	pages = {1{\textendash}19},
	doi = {https://doi.org/10.1002/widm.1356},
	author = {Failla, Andrea and Citraro, Salvatore and Rossetti, Giulio}
}
@conference {1557,
	title = {Attributed stream-hypernetwork analysis: homophilic behaviors in pairwise and group political discussions on reddit},
	booktitle = {International Conference on Complex Networks and Their Applications},
	year = {2022},
	publisher = {Springer},
	organization = {Springer},
	doi = {https://doi.org/10.1007/978-3-031-21127-0_13},
	author = {Failla, Andrea and Citraro, Salvatore and Rossetti, Giulio}
}
@conference {1485,
	title = {From Mean-Field to Complex Topologies: Network Effects on the Algorithmic Bias Model},
	booktitle = {Complex Networks \& Their Applications X},
	year = {2022},
	author = {Valentina Pansanella and Giulio Rossetti and Letizia Milli}
}
@article {1484,
	title = {Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities},
	journal = {arXiv preprint arXiv:2110.15269},
	year = {2021},
	author = {Joseph, Simmi Marina and Salvatore Citraro and Morini, Virginia and Giulio Rossetti and Stella, Massimo}
}
@article {1429,
	title = {Conformity: a Path-Aware Homophily measure for Node-Attributed Networks},
	journal = {IEEE Intelligent SystemsIEEE Intelligent Systems},
	year = {2021},
	month = {2021},
	pages = {1 - 1},
	abstract = {Unveil 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.},
	isbn = {1941-1294},
	doi = {https://doi.org/10.1109/MIS.2021.3051291},
	url = {https://ieeexplore.ieee.org/document/9321348},
	author = {Giulio Rossetti and Salvatore Citraro and Letizia Milli}
}
@article {1483,
	title = {Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study},
	journal = {Applied Sciences},
	volume = {11},
	number = {12},
	year = {2021},
	pages = {5390},
	author = {Morini, Virginia and Pollacci, Laura and Giulio Rossetti}
}
@article {1370,
	title = {ANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks},
	journal = {Applied Network Science},
	volume = {5},
	number = {1},
	year = {2020},
	pages = {1{\textendash}23},
	abstract = {Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.},
	doi = {https://doi.org/10.1007/s41109-020-00270-6},
	url = {https://link.springer.com/article/10.1007/s41109-020-00270-6},
	author = {Giulio Rossetti}
}
@conference {1371,
	title = {Capturing Political Polarization of Reddit Submissions in the Trump Era},
	booktitle = {SEBD},
	year = {2020},
	author = {Giulio Rossetti and Morini, Virginia and Pollacci, Laura}
}
@article {1364,
	title = {Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks},
	journal = {arXiv preprint arXiv:2012.05195},
	year = {2020},
	author = {Giulio Rossetti and Salvatore Citraro and Letizia Milli}
}
@article {1369,
	title = {Evaluating community detection algorithms for progressively evolving graphs},
	journal = {arXiv preprint arXiv:2007.08635},
	year = {2020},
	author = {Cazabet, R{\'e}my and Boudebza, Souaad and Giulio Rossetti}
}
@article {1358,
	title = {Identifying and exploiting homogeneous communities in labeled networks},
	journal = {Applied Network Science},
	volume = {5},
	number = {1},
	year = {2020},
	pages = {1{\textendash}20},
	abstract = {Attribute-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.},
	doi = {https://doi.org/10.1007/s41109-020-00302-1},
	url = {https://appliednetsci.springeropen.com/articles/10.1007/s41109-020-00302-1},
	author = {Salvatore Citraro and Giulio Rossetti}
}
@conference {1409,
	title = {{\textquotedblleft}Know Thyself{\textquotedblright} How Personal Music Tastes Shape the Last.Fm Online Social Network},
	booktitle = {Formal Methods. FM 2019 International Workshops},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {As Nietzsche once wrote {\textquotedblleft}Without music, life would be a mistake{\textquotedblright} (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?.},
	isbn = {978-3-030-54994-7},
	doi = {https://doi.org/10.1007/978-3-030-54994-7_11},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-54994-7_11},
	author = {Riccardo Guidotti and Giulio Rossetti},
	editor = {Sekerinski, Emil and Moreira, Nelma and Oliveira, Jos{\'e} N. and Ratiu, Daniel and Riccardo Guidotti and Farrell, Marie and Luckcuck, Matt and Marmsoler, Diego and Campos, Jos{\'e} and Astarte, Troy and Gonnord, Laure and Cerone, Antonio and Couto, Luis and Dongol, Brijesh and Kutrib, Martin and Monteiro, Pedro and Delmas, David}
}
@booklet {1425,
	title = {Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown},
	year = {2020},
	abstract = {Understanding 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?},
	doi = {https://dx.doi.org/10.32079/ISTI-TR-2020/005},
	url = {https://arxiv.org/abs/2004.11278},
	author = {Pietro Bonato and Paolo Cintia and Francesco Fabbri and Daniele Fadda and Fosca Giannotti and Pier Luigi Lopalco and Sara Mazzilli and Mirco Nanni and Luca Pappalardo and Dino Pedreschi and Francesco Penone and S Rinzivillo and Giulio Rossetti and Marcello Savarese and Lara Tavoschi}
}
@article {1368,
	title = {Modelling Human Mobility considering Spatial, Temporal and Social Dimensions},
	journal = {arXiv preprint arXiv:2007.02371},
	year = {2020},
	author = {Cornacchia, Giuliano and Giulio Rossetti and Luca Pappalardo}
}
@conference {1365,
	title = {Opinion Dynamic Modeling of Fake News Perception},
	booktitle = {International Conference on Complex Networks and Their Applications},
	year = {2020},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Fake 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{\textquoteright} 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.},
	doi = {https://doi.org/10.1007/978-3-030-65347-7_31},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-65347-7_31},
	author = {Toccaceli, Cecilia and Letizia Milli and Giulio Rossetti}
}
@article {1339,
	title = {The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy},
	journal = {arXiv preprint arXiv:2006.03141},
	year = {2020},
	abstract = {We 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{\textquoteright}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.},
	url = {https://arxiv.org/abs/2006.03141},
	author = {Paolo Cintia and Daniele Fadda and Fosca Giannotti and Luca Pappalardo and Giulio Rossetti and Dino Pedreschi and S Rinzivillo and Bonato, Pietro and Fabbri, Francesco and Penone, Francesco and Savarese, Marcello and Checchi, Daniele and Chiaromonte, Francesca and Vineis , Paolo and Guzzetta, Giorgio and Riccardo, Flavia and Marziano, Valentina and Poletti, Piero and Trentini, Filippo and Bella, Antonio and Andrianou, Xanthi and Del Manso, Martina and Fabiani, Massimo and Bellino, Stefania and Boros, Stefano and Mateo Urdiales, Alberto and Vescio, Maria Fenicia and Brusaferro, Silvio and Rezza, Giovanni and Pezzotti, Patrizio and Ajelli, Marco and Merler, Stefano}
}
@article {1367,
	title = {UTLDR: an agent-based framework for modeling infectious diseases and public interventions},
	journal = {arXiv preprint arXiv:2011.05606},
	year = {2020},
	author = {Giulio Rossetti and Letizia Milli and Salvatore Citraro and Morini, Virginia}
}
@article {1407,
	title = {CDLIB: a python library to extract, compare and evaluate communities from complex networks},
	journal = {Applied Network Science},
	volume = {4},
	year = {2019},
	month = {2019/07/29},
	pages = {52},
	abstract = {Community 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.},
	isbn = {2364-8228},
	doi = {https://doi.org/10.1007/s41109-019-0165-9},
	url = {https://link.springer.com/article/10.1007/s41109-019-0165-9},
	author = {Giulio Rossetti and Letizia Milli and Cazabet, R{\'e}my}
}
@inbook {1293,
	title = {Challenges in community discovery on temporal networks},
	booktitle = {Temporal Network Theory},
	year = {2019},
	pages = {181{\textendash}197},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Community 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.},
	doi = {10.1007/978-3-030-23495-9_10},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-23495-9_10},
	author = {Cazabet, R{\'e}my and Giulio Rossetti}
}
@conference {1372,
	title = {Community-Aware Content Diffusion: Embeddednes and Permeability},
	booktitle = {International Conference on Complex Networks and Their Applications},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	author = {Letizia Milli and Giulio Rossetti}
}
@conference {1379,
	title = {A complex network approach to semantic spaces: How meaning organizes itself},
	booktitle = {SEBD},
	year = {2019},
	author = {Salvatore Citraro and Giulio Rossetti}
}
@article {1377,
	title = {DynComm R Package{\textendash}Dynamic Community Detection for Evolving Networks},
	journal = {arXiv preprint arXiv:1905.01498},
	year = {2019},
	author = {Sarmento, Rui Portocarrero and Lemos, Lu{\'\i}s and Cordeiro, M{\'a}rio and Giulio Rossetti and Cardoso, Douglas}
}
@conference {1357,
	title = {Eva: Attribute-Aware Network Segmentation},
	booktitle = {International Conference on Complex Networks and Their Applications},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Identifying 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.},
	author = {Salvatore Citraro and Giulio Rossetti}
}
@conference {1373,
	title = {Exorcising the Demon: Angel, Efficient Node-Centric Community Discovery},
	booktitle = {International Conference on Complex Networks and Their Applications},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	author = {Giulio Rossetti}
}
@conference {1376,
	title = {{\textquotedblleft}Know Thyself{\textquotedblright} How Personal Music Tastes Shape the Last. Fm Online Social Network},
	booktitle = {International Symposium on Formal Methods},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	author = {Riccardo Guidotti and Giulio Rossetti}
}
@article {1382,
	title = {Towards the dynamic community discovery in decentralized online social networks},
	journal = {Journal of Grid Computing},
	volume = {17},
	number = {1},
	year = {2019},
	pages = {23{\textendash}44},
	author = {Guidi, Barbara and Michienzi, Andrea and Giulio Rossetti}
}
@article {1179,
	title = {Active and passive diffusion processes in complex networks},
	journal = {Applied network science},
	volume = {3},
	number = {1},
	year = {2018},
	pages = {42},
	abstract = {Ideas, 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.},
	doi = {https://doi.org/10.1007/s41109-018-0100-5},
	url = {https://link.springer.com/article/10.1007/s41109-018-0100-5},
	author = {Letizia Milli and Giulio Rossetti and Dino Pedreschi and Fosca Giannotti}
}
@article {999,
	title = {Community Discovery in Dynamic Networks: a Survey},
	journal = {Journal ACM Computing Surveys},
	volume = {51},
	number = {2},
	year = {2018},
	abstract = {Networks 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. },
	doi = {10.1145/3172867},
	url = {https://dl.acm.org/citation.cfm?id=3172867},
	author = {Giulio Rossetti and Cazabet, R{\'e}my}
}
@conference {1021,
	title = {Diffusive Phenomena in Dynamic Networks: a data-driven study},
	booktitle = {International Conference on Complex Networks CompleNet},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	address = {Boston March 5-8 2018},
	abstract = {Everyday, 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 {\textendash} following a data-driven approach {\textendash} 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.},
	doi = {10.1007/978-3-319-73198-8_13},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-73198-8_13},
	author = {Letizia Milli and Giulio Rossetti and Dino Pedreschi and Fosca Giannotti}
}
@conference {1046,
	title = {Discovering Mobility Functional Areas: A Mobility Data Analysis Approach},
	booktitle = {International Workshop on Complex Networks},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	abstract = {How do we measure the borders of urban areas and therefore decide which are the functional units of the territory? Nowadays, we typically do that just looking at census data, while in this work we aim to identify functional areas for mobility in a completely data-driven way. Our solution makes use of human mobility data (vehicle trajectories) and consists in an agglomerative process which gradually groups together those municipalities that maximize internal vehicular traffic while minimizing external one. The approach is tested against a dataset of trips involving individuals of an Italian Region, obtaining a new territorial division which allows us to identify mobility attractors. Leveraging such partitioning and external knowledge, we show that our method outperforms the state-of-the-art algorithms. Indeed, the outcome of our approach is of great value to public administrations for creating synergies within the aggregations of the territories obtained.},
	doi = {10.1007/978-3-319-73198-8_27},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-73198-8_27},
	author = {Lorenzo Gabrielli and Daniele Fadda and Giulio Rossetti and Mirco Nanni and Piccinini, Leonardo and Dino Pedreschi and Fosca Giannotti and Patrizia Lattarulo}
}
@conference {1039,
	title = {The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis},
	booktitle = {International Conference on Smart Objects and Technologies for Social Good},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Nowadays there is a growing standardization of musical contents. Our finding comes out from a cross-service multi-level dataset analysis where we study how geography affects the music production. The investigation presented in this paper highlights the existence of a {\textquotedblleft}fractal{\textquotedblright} musical structure that relates the technical characteristics of the music produced at regional, national and world level. Moreover, a similar structure emerges also when we analyze the musicians{\textquoteright} popularity and the polarity of their songs defined as the mood that they are able to convey. Furthermore, the clusters identified are markedly distinct one from another with respect to popularity and sentiment.},
	doi = {10.1007/978-3-319-76111-4_19},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-76111-4_19},
	author = {Pollacci, Laura and Riccardo Guidotti and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@inbook {1422,
	title = {How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science},
	booktitle = {A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years},
	year = {2018},
	pages = {287 - 306},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {During 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.},
	isbn = {978-3-319-61893-7},
	doi = {https://doi.org/10.1007/978-3-319-61893-7_17},
	url = {https://link.springer.com/chapter/10.1007\%2F978-3-319-61893-7_17},
	author = {Amato, G. and Candela, L. and Castelli, D. and Esuli, A. and Falchi, F. and Gennaro, C. and Fosca Giannotti and Anna Monreale and Mirco Nanni and Pagano, P. and Luca Pappalardo and Dino Pedreschi and Francesca Pratesi and Rabitti, F. and S Rinzivillo and Giulio Rossetti and Salvatore Ruggieri and Sebastiani, F. and Tesconi, M.},
	editor = {Flesca, Sergio and Greco, Sergio and Masciari, Elio and Sacc{\`a}, Domenico}
}
@article {1133,
	title = {The italian music superdiversity},
	journal = {Multimedia Tools and Applications},
	year = {2018},
	pages = {1{\textendash}23},
	abstract = {Globalization can lead to a growing standardization of musical contents. Using a cross-service multi-level dataset we investigate the actual Italian music scene. The investigation highlights the musical Italian superdiversity both individually analyzing the geographical and lexical dimensions and combining them. Using different kinds of features over the geographical dimension leads to two similar, comparable and coherent results, confirming the strong and essential correlation between melodies and lyrics. The profiles identified are markedly distinct one from another with respect to sentiment, lexicon, and melodic features. Through a novel application of a sentiment spreading algorithm and songs{\textquoteright} melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices.},
	doi = {10.1007/s11042-018-6511-6},
	url = {https://link.springer.com/article/10.1007/s11042-018-6511-6},
	author = {Pollacci, Laura and Riccardo Guidotti and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@article {1047,
	title = {NDlib: a python library to model and analyze diffusion processes over complex networks},
	journal = {International Journal of Data Science and Analytics},
	volume = {5},
	number = {1},
	year = {2018},
	pages = {61{\textendash}79},
	abstract = {Nowadays 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.},
	doi = {10.1007/s41060-017-0086-6},
	url = {https://link.springer.com/article/10.1007/s41060-017-0086-6},
	author = {Giulio Rossetti and Letizia Milli and S Rinzivillo and Alina Sirbu and Dino Pedreschi and Fosca Giannotti}
}
@article {1134,
	title = {Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences},
	journal = {IEEE Transactions on Knowledge and Data Engineering},
	year = {2018},
	abstract = {Nowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer{\textquoteright}s decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer{\textquoteright}s stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.},
	doi = {10.1109/TKDE.2018.2872587},
	url = {https://ieeexplore.ieee.org/abstract/document/8477157},
	author = {Riccardo Guidotti and Giulio Rossetti and Luca Pappalardo and Fosca Giannotti and Dino Pedreschi}
}
@conference {1390,
	title = {Dynamic community analysis in decentralized online social networks},
	booktitle = {European Conference on Parallel Processing},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	author = {Guidi, Barbara and Michienzi, Andrea and Giulio Rossetti}
}
@article {1018,
	title = {Forecasting success via early adoptions analysis: A data-driven study},
	journal = {PloS one},
	volume = {12},
	number = {12},
	year = {2017},
	pages = {e0189096},
	abstract = {Innovations 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{\textquoteright}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.},
	author = {Giulio Rossetti and Letizia Milli and Fosca Giannotti and Dino Pedreschi}
}
@conference {1129,
	title = {The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis},
	booktitle = {International Conference on Smart Objects and Technologies for Social Good},
	year = {2017},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Nowadays there is a growing standardization of musical contents. Our finding comes out from a cross-service multi-level dataset analysis where we study how geography affects the music production. The investigation presented in this paper highlights the existence of a {\textquotedblleft}fractal{\textquotedblright} musical structure that relates the technical characteristics of the music produced at regional, national and world level. Moreover, a similar structure emerges also when we analyze the musicians{\textquoteright} popularity and the polarity of their songs defined as the mood that they are able to convey. Furthermore, the clusters identified are markedly distinct one from another with respect to popularity and sentiment.

},
	doi = {https://doi.org/10.1007/978-3-319-76111-4_19},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-76111-4_19},
	author = {Pollacci, Laura and Riccardo Guidotti and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@conference {1019,
	title = {Information diffusion in complex networks: The active/passive conundrum},
	booktitle = {International Workshop on Complex Networks and their Applications},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Ideas, 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.},
	doi = {10.1007/978-3-319-72150-7_25},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-72150-7_25},
	author = {Letizia Milli and Giulio Rossetti and Dino Pedreschi and Fosca Giannotti}
}
@conference {1024,
	title = {Market Basket Prediction using User-Centric Temporal Annotated Recurring Sequences},
	booktitle = {2017 IEEE International Conference on Data Mining (ICDM)},
	year = {2017},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Nowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer{\textquoteright}s decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern named Temporal Annotated Recurring Sequence (TARS). We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer{\textquoteright}s stocks and recommend the set of most necessary items. A deep experimentation shows that TARS can explain the customers{\textquoteright} purchase behavior, and that TBP outperforms the state-of-the-art competitors.},
	author = {Riccardo Guidotti and Giulio Rossetti and Luca Pappalardo and Fosca Giannotti and Dino Pedreschi}
}
@article {1020,
	title = {NDlib: a python library to model and analyze diffusion processes over complex networks},
	journal = {International Journal of Data Science and Analytics},
	year = {2017},
	pages = {1{\textendash}19},
	abstract = {Nowadays 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.},
	author = {Giulio Rossetti and Letizia Milli and S Rinzivillo and Alina Sirbu and Dino Pedreschi and Fosca Giannotti}
}
@conference {1022,
	title = {NDlib: Studying Network Diffusion Dynamics},
	booktitle = {IEEE International Conference on Data Science and Advanced Analytics, DSA},
	year = {2017},
	address = {Tokyo},
	abstract = {Nowadays 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.},
	doi = {https://doi.org/10.1109/DSAA.2017.6},
	url = {https://ieeexplore.ieee.org/abstract/document/8259774},
	author = {Giulio Rossetti and Letizia Milli and S Rinzivillo and Alina Sirbu and Dino Pedreschi and Fosca Giannotti}
}
@article {957,
	title = {Next Basket Prediction using Recurring Sequential Patterns},
	journal = {arXiv preprint arXiv:1702.07158},
	year = {2017},
	abstract = {Nowadays, a hot challenge for supermarket chains is to offer personalized services for their customers. Next basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable to capture at the same time the different factors influencing the customer{\textquoteright}s decision process: co-occurrency, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to to understand the level of the customer{\textquoteright}s stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.},
	url = {https://arxiv.org/abs/1702.07158},
	author = {Riccardo Guidotti and Giulio Rossetti and Luca Pappalardo and Fosca Giannotti and Dino Pedreschi}
}
@article {1025,
	title = {Node-centric Community Discovery: From static to dynamic social network analysis},
	journal = {Online Social Networks and Media},
	volume = {3},
	year = {2017},
	pages = {32{\textendash}48},
	abstract = {Nowadays, online social networks represent privileged playgrounds that enable researchers to study, characterize and understand complex human behaviors. Social Network Analysis, commonly known as SNA, is the multidisciplinary field of research under which researchers of different backgrounds perform their studies: one of the hottest topics in such diversified context is indeed Community Discovery. Clustering individuals, whose relations are described by a networked structure, into homogeneous communities is a complex task required by several analytical processes. Moreover, due to the user-centric and dynamic nature of online social services, during the last decades, particular emphasis was dedicated to the definition of node-centric, overlapping and evolutive Community Discovery methodologies.
In this paper we provide a comprehensive and concise review of the main results, both algorithmic and analytical, we obtained in this field. Moreover, to better underline the rationale behind our research activity on Community Discovery, in this work we provide a synthetic review of the relevant literature, discussing not only methodological results but also analytical ones.},
	doi = {https://doi.org/10.1016/j.osnem.2017.10.003},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S2468696417301052},
	author = {Giulio Rossetti and Dino Pedreschi and Fosca Giannotti}
}
@article {954,
	title = {Tiles: an online algorithm for community discovery in dynamic social networks},
	journal = {Machine Learning},
	volume = {106},
	number = {8},
	year = {2017},
	pages = {1213{\textendash}1241},
	abstract = {Community discovery has emerged during the last decade as one of the most challenging problems in social network analysis. Many algorithms have been proposed to find communities on static networks, i.e. networks which do not change in time. However, social networks are dynamic realities (e.g. call graphs, online social networks): in such scenarios static community discovery fails to identify a partition of the graph that is semantically consistent with the temporal information expressed by the data. In this work we propose Tiles, an algorithm that extracts overlapping communities and tracks their evolution in time following an online iterative procedure. Our algorithm operates following a domino effect strategy, dynamically recomputing nodes community memberships whenever a new interaction takes place. We compare Tiles with state-of-the-art community detection algorithms on both synthetic and real world networks having annotated community structure: our experiments show that the proposed approach is able to guarantee lower execution times and better correspondence with the ground truth communities than its competitors. Moreover, we illustrate the specifics of the proposed approach by discussing the properties of identified communities it is able to identify.},
	doi = {10.1007/s10994-016-5582-8},
	url = {https://link.springer.com/article/10.1007/s10994-016-5582-8},
	author = {Giulio Rossetti and Luca Pappalardo and Dino Pedreschi and Fosca Giannotti}
}
@proceedings {874,
	title = {{\textquotedblleft}Are we playing like Music-Stars?{\textquotedblright} Placing Emerging Artists on the Italian Music Scene},
	year = {2016},
	month = {09/2016},
	address = {Riva del Garda},
	abstract = {The Italian emerging bands chase success on the footprint of popular artists by playing rhythmic danceable and happy songs. Our finding comes out from a deep study of the Italian music scene and how the new generation ofmusicians relate with the tradition of their country. By analyzing Spotify data we investigated the peculiarity of regional mu- sic and we placed emerging bands within the musical movements defined by already successful artists. The approach proposed and the results ob- tained are a first attempt to outline some rules suggesting how to reach the success in the musical Italian scene.},
	author = {Pollacci, Laura and Riccardo Guidotti and Giulio Rossetti}
}
@conference {882,
	title = {Audio Ergo Sum},
	booktitle = {Federation of International Conferences on Software Technologies: Applications and Foundations},
	year = {2016},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Nobody can state {\textquotedblleft}Rock is my favorite genre{\textquotedblright} or {\textquotedblleft}David Bowie is my favorite artist{\textquotedblright}. We defined a Personal Listening Data Model able to capture musical preferences through indicators and patterns, and we discovered that we are all characterized by a limited set of musical preferences, but not by a unique predilection. The empowered capacity of mobile devices and their growing adoption in our everyday life is generating an enormous increment in the production of personal data such as calls, positioning, online purchases and even music listening. Musical listening is a type of data that has started receiving more attention from the scientific community as consequence of the increasing availability of rich and punctual online data sources. Starting from the listening of 30k Last.Fm users, we show how the employment of the Personal Listening Data Models can provide higher levels of self-awareness. In addition, the proposed model will enable the development of a wide range of analysis and musical services both at personal and at collective level.},
	doi = {10.1007/978-3-319-50230-4_5},
	author = {Riccardo Guidotti and Giulio Rossetti and Dino Pedreschi}
}
@article {960,
	title = {Homophilic network decomposition: a community-centric analysis of online social services},
	journal = {Social Network Analysis and Mining},
	volume = {6},
	number = {1},
	year = {2016},
	pages = {103},
	abstract = {In this paper we formulate the homophilic network decomposition problem: Is it possible to identify a network partition whose structure is able to characterize the degree of homophily of its nodes? The aim of our work is to understand the relations between the homophily of individuals and the topological features expressed by specific network substructures. We apply several community detection algorithms on three large-scale online social networks{\textemdash}Skype, LastFM and Google+{\textemdash}and advocate the need of identifying the right algorithm for each specific network in order to extract a homophilic network decomposition. Our results show clear relations between the topological features of communities and the degree of homophily of their nodes in three online social scenarios: product engagement in the Skype network, number of listened songs on LastFM and homogeneous level of education among users of Google+.},
	doi = {10.1007/s1327},
	author = {Giulio Rossetti and Luca Pappalardo and Riivo Kikas and Dino Pedreschi and Fosca Giannotti and Marlon Dumas}
}
@conference {822,
	title = {A novel approach to evaluate community detection algorithms on ground truth},
	booktitle = {7th Workshop on Complex Networks},
	year = {2016},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Dijon, France},
	abstract = {Evaluating a community detection algorithm is a complex task due to the lack of a shared and universally accepted definition of community. In literature, one of the most common way to assess the performances of a community detection algorithm is to compare its output with given ground truth communities by using computationally expensive metrics (i.e., Normalized Mutual Information). In this paper we propose a novel approach aimed at evaluating the adherence of a community partition to the ground truth: our methodology provides more information than the state-of-the-art ones and is fast to compute on large-scale networks. We evaluate its correctness by applying it to six popular community detection algorithms on four large-scale network datasets. Experimental results show how our approach allows to easily evaluate the obtained communities on the ground truth and to characterize the quality of community detection algorithms.},
	doi = {10.1007/978-3-319-30569-1_10},
	url = {http://www.giuliorossetti.net/about/wp-content/uploads/2015/12/Complenet16.pdf},
	author = {Giulio Rossetti and Luca Pappalardo and S Rinzivillo}
}
@article {866,
	title = {A supervised approach for intra-/inter-community interaction prediction in dynamic social networks},
	journal = {Social Network Analysis and Mining},
	volume = {6},
	number = {1},
	year = {2016},
	month = {09/2016},
	pages = {86},
	abstract = {Due to the growing availability of Internet services in the last decade, the interactions between people became more and more easy to establish. For example, we can have an intercontinental job interview, or we can send real-time multimedia content to any friend of us just owning a smartphone. All this kind of human activities generates digital footprints, that describe a complex, rapidly evolving, network structures. In such dynamic scenario, one of the most challenging tasks involves the prediction of future interactions between couples of actors (i.e., users in online social networks, researchers in collaboration networks). In this paper, we approach such problem by leveraging networks dynamics: to this extent, we propose a supervised learning approach which exploits features computed by time-aware forecasts of topological measures calculated between node pairs. Moreover, since real social networks are generally composed by weakly connected modules, we instantiate the interaction prediction problem in two disjoint applicative scenarios: intra-community and inter-community link prediction. Experimental results on real time-stamped networks show how our approach is able to reach high accuracy. Furthermore, we analyze the performances of our methodology when varying the typologies of features, community discovery algorithms and forecast methods.},
	issn = {1869-5469},
	doi = {10.1007/s13278-016-0397-y},
	url = {http://dx.doi.org/10.1007/s13278-016-0397-y},
	author = {Giulio Rossetti and Riccardo Guidotti and Ioanna Miliou and Dino Pedreschi and Fosca Giannotti}
}
@conference {819,
	title = {Community-centric analysis of user engagement in Skype social network},
	booktitle = {International conference on Advances in Social Network Analysis and Mining},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Paris, France},
	isbn = {978-1-4503-3854-7},
	doi = {10.1145/2808797.2809384},
	url = {http://dl.acm.org/citation.cfm?doid=2808797.2809384},
	author = {Giulio Rossetti and Luca Pappalardo and Riivo Kikas and Dino Pedreschi and Fosca Giannotti and Marlon Dumas}
}
@conference {816,
	title = {Interaction Prediction in Dynamic Networks exploiting Community Discovery},
	booktitle = {International conference on Advances in Social Network Analysis and Mining, ASONAM 2015},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Paris, France},
	abstract = {Due to the growing availability of online social services, interactions between people became more and more easy to establish and track. Online social human activities generate digital footprints, that describe complex, rapidly evolving, dynamic networks. In such scenario one of the most challenging task to address involves the prediction of future interactions between couples of actors. In this study, we want to leverage networks dynamics and community structure to predict which are the future interactions more likely to appear. To this extent, we propose a supervised learning approach which exploit features computed by time-aware forecasts of topological measures calculated between pair of nodes belonging to the same community. Our experiments on real dynamic networks show that the designed analytical process is able to achieve interesting results.},
	isbn = {978-1-4503-3854-7},
	doi = {0.1145/2808797.2809401},
	url = {http://dl.acm.org/citation.cfm?doid=2808797.2809401},
	author = {Giulio Rossetti and Riccardo Guidotti and Diego Pennacchioli and Dino Pedreschi and Fosca Giannotti}
}
@conference {820,
	title = {Quantification in Social Networks},
	booktitle = {International Conference on Data Science and Advanced Analytics (IEEE DSAA{\textquoteright}2015)},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Paris, France},
	abstract = {In 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. },
	doi = {10.1109/DSAA.2015.7344845},
	url = {http://www.giuliorossetti.net/about/wp-content/uploads/2015/12/main_DSAA.pdf},
	author = {Letizia Milli and Anna Monreale and Giulio Rossetti and Dino Pedreschi and Fosca Giannotti and Fabrizio Sebastiani}
}
@conference {623,
	title = {The patterns of musical influence on the Last.Fm social network},
	booktitle = {22nd Italian Symposium on Advanced Database Systems, {SEBD} 2014, Sorrento Coast, Italy, June 16-18, 2014.},
	year = {2014},
	author = {Diego Pennacchioli and Giulio Rossetti and Luca Pappalardo and Dino Pedreschi and Fosca Giannotti and Michele Coscia}
}
@article {622,
	title = {Uncovering Hierarchical and Overlapping Communities with a Local-First Approach},
	journal = {{TKDD}},
	volume = {9},
	number = {1},
	year = {2014},
	pages = {6},
	abstract = {Community discovery in complex networks is the task of organizing a network{\textquoteright}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.},
	doi = {10.1145/2629511},
	url = {http://doi.acm.org/10.1145/2629511},
	author = {Michele Coscia and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@conference {504,
	title = {Measuring tie strength in multidimensional networks},
	booktitle = {SEDB 2013},
	year = {2013},
	month = {2013},
	author = {Giulio Rossetti and Luca Pappalardo and Dino Pedreschi}
}
@conference {505,
	title = {On multidimensional network measures},
	booktitle = {SEDB 2013},
	year = {2013},
	month = {2013},
	abstract = {Networks, i.e., sets of interconnected entities, are ubiquitous,
spanning disciplines as diverse as sociology, biology and computer science.
The recent availability of large amounts of network data has thus
provided a unique opportunity to develop models and analysis tools applicable
to a wide range of scenarios. However, real-world phenomena are
often more complex than existing graph data models. One relevant example
concerns the numerous types of social relationships (or edges) that
can be present between individuals in a social network. In this short paper
we present a unified model and a set of measures recently developed
to represent and analyze network data with multiple types of edges.},
	url = {https://www.researchgate.net/publication/256194479_On_multidimensional_network_measures},
	author = {Matteo Magnani and Anna Monreale and Giulio Rossetti and Fosca Giannotti}
}
@conference {563,
	title = {Quantification Trees},
	booktitle = {2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA, December 7-10, 2013},
	year = {2013},
	pages = {528{\textendash}536},
	abstract = {In 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.},
	doi = {10.1109/ICDM.2013.122},
	url = {http://dx.doi.org/10.1109/ICDM.2013.122},
	author = {Letizia Milli and Anna Monreale and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi and Fabrizio Sebastiani}
}
@conference {624,
	title = {The Three Dimensions of Social Prominence},
	booktitle = {Social Informatics - 5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013, Proceedings},
	year = {2013},
	doi = {10.1007/978-3-319-03260-3_28},
	url = {http://dx.doi.org/10.1007/978-3-319-03260-3_28},
	author = {Diego Pennacchioli and Giulio Rossetti and Luca Pappalardo and Dino Pedreschi and Fosca Giannotti and Michele Coscia}
}
@conference {502,
	title = {You Know Because I Know{\textquotedblright}: a Multidimensional Network Approach to Human Resources Problem},
	booktitle = {ASONAM 2013},
	year = {2013},
	author = {Michele Coscia and Giulio Rossetti and Diego Pennacchioli and Damiano Ceccarelli and Fosca Giannotti}
}
@conference {625,
	title = {DEMON: a local-first discovery method for overlapping communities},
	booktitle = {The 18th {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining, {KDD} {\textquoteright}12, Beijing, China, August 12-16, 2012},
	year = {2012},
	doi = {10.1145/2339530.2339630},
	url = {http://doi.acm.org/10.1145/2339530.2339630},
	author = {Michele Coscia and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@conference {507,
	title = {DEMON: a Local-First Discovery Method for Overlapping Communities},
	booktitle = {KDD 2012},
	year = {2012},
	month = {2012},
	author = {Michele Coscia and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@conference {626,
	title = {"How Well Do We Know Each Other?" Detecting Tie Strength in Multidimensional Social Networks},
	booktitle = {International Conference on Advances in Social Networks Analysis and Mining, {ASONAM} 2012, Istanbul, Turkey, 26-29 August 2012},
	year = {2012},
	doi = {10.1109/ASONAM.2012.180},
	url = {http://doi.ieeecomputersociety.org/10.1109/ASONAM.2012.180},
	author = {Luca Pappalardo and Giulio Rossetti and Dino Pedreschi}
}
@conference {627,
	title = {Link Prediction su Reti Multidimensionali},
	booktitle = {Sistemi Evoluti per Basi di Dati - {SEBD} 2011, Proceedings of the Nineteenth Italian Symposium on Advanced Database Systems, Maratea, Italy, June 26-29, 2011},
	year = {2011},
	author = {Giulio Rossetti and Michele Berlingerio and Fosca Giannotti}
}
@conference {446,
	title = {Scalable Link Prediction on Multidimensional Networks},
	booktitle = {ICDM Workshops},
	year = {2011},
	pages = {979-986},
	address = {Vancouver},
	author = {Giulio Rossetti and Michele Berlingerio and Fosca Giannotti}
}
