%0 Book Section %B Complex Networks VII %D 2016 %T Where Is My Next Friend? Recommending Enjoyable Profiles in Location Based Services %A Riccardo Guidotti %A Michele Berlingerio %X How many of your friends, with whom you enjoy spending some time, live close by? How many people are at your reach, with whom you could have a nice conversation? We introduce a measure of enjoyability that may be the basis for a new class of location-based services aimed at maximizing the likelihood that two persons, or a group of people, would enjoy spending time together. Our enjoyability takes into account both topic similarity between two users and the users’ tendency to connect to people with similar or dissimilar interest. We computed the enjoyability on two datasets of geo-located tweets, and we reasoned on the applicability of the obtained results for producing friend recommendations. We aim at suggesting couples of users which are not friends yet, but which are frequently co-located and maximize our enjoyability measure. By taking into account the spatial dimension, we show how 50 % of users may find at least one enjoyable person within 10 km of their two most visited locations. Our results are encouraging, and open the way for a new class of recommender systems based on enjoyability. %B Complex Networks VII %I Springer International Publishing %P 65–78 %G eng %R 10.1007/978-3-319-30569-1_5 %0 Conference Paper %B Machine Learning and Knowledge Discovery in Databases %D 2015 %T Mobility Mining for Journey Planning in Rome %A Michele Berlingerio %A Bicer, Veli %A Botea, Adi %A Braghin, Stefano %A Lopes, Nuno %A Riccardo Guidotti %A Francesca Pratesi %X We present recent results on integrating private car GPS routines obtained by a Data Mining module. into the PETRA (PErsonal TRansport Advisor) platform. The routines are used as additional “bus lines”, available to provide a ride to travelers. We present the effects of querying the planner with and without the routines, which show how Data Mining may help Smarter Cities applications. %B Machine Learning and Knowledge Discovery in Databases %I Springer International Publishing %G eng %0 Conference Paper %B Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on %D 2015 %T Social or green? A data-driven approach for more enjoyable carpooling %A Riccardo Guidotti %A Sassi, Andrea %A Michele Berlingerio %A Pascale, Alessandra %A Ghaddar, Bissan %B Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on %I IEEE %G eng %0 Journal Article %J Intell. Data Anal. %D 2013 %T Evolving networks: Eras and turning points %A Michele Berlingerio %A Michele Coscia %A Fosca Giannotti %A Anna Monreale %A Dino Pedreschi %X Within 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. %B Intell. Data Anal. %V 17 %P 27–48 %U http://dx.doi.org/10.3233/IDA-120566 %R 10.3233/IDA-120566 %0 Conference Proceedings %B MDM 2012 %D 2012 %T ComeTogether: Discovering Communities of Places in Mobility Data %A Igo Brilhante %A Michele Berlingerio %A Roberto Trasarti %A Chiara Renso %A de José Antônio Fernandes Macêdo %A Marco A. Casanova %B MDM 2012 %P 268-273 %8 2012 %0 Journal Article %J World Wide Web %D 2012 %T Multidimensional networks: foundations of structural analysis %A Michele Berlingerio %A Michele Coscia %A Fosca Giannotti %A Anna Monreale %A Dino Pedreschi %X Complex 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. %B World Wide Web %V Volume 15 / 2012 %8 10/2012 %U http://www.springerlink.com/content/f774289854430410/abstract/ %R 10.1007/s11280-012-0190-4 %0 Conference Paper %B ASONAM %D 2011 %T Finding and Characterizing Communities in Multidimensional Networks %A Michele Berlingerio %A Michele Coscia %A Fosca Giannotti %B ASONAM %P 490-494 %0 Conference Paper %B CIKM %D 2011 %T Finding redundant and complementary communities in multidimensional networks %A Michele Berlingerio %A Michele Coscia %A Fosca Giannotti %B CIKM %P 2181-2184 %0 Conference Paper %B ASONAM %D 2011 %T Foundations of Multidimensional Network Analysis %A Michele Berlingerio %A Michele Coscia %A Fosca Giannotti %A Anna Monreale %A Dino Pedreschi %X Complex 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. %B ASONAM %P 485-489 %R 10.1109/ASONAM.2011.103 %0 Conference Paper %B Sistemi Evoluti per Basi di Dati - {SEBD} 2011, Proceedings of the Nineteenth Italian Symposium on Advanced Database Systems, Maratea, Italy, June 26-29, 2011 %D 2011 %T Link Prediction su Reti Multidimensionali %A Giulio Rossetti %A Michele Berlingerio %A Fosca Giannotti %B Sistemi Evoluti per Basi di Dati - {SEBD} 2011, Proceedings of the Nineteenth Italian Symposium on Advanced Database Systems, Maratea, Italy, June 26-29, 2011 %G eng %0 Journal Article %J J. Comput. Science %D 2011 %T The pursuit of hubbiness: Analysis of hubs in large multidimensional networks %A Michele Berlingerio %A Michele Coscia %A Fosca Giannotti %A Anna Monreale %A Dino Pedreschi %X Hubs 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. %B J. Comput. Science %V 2 %P 223-237 %R 10.1016/j.jocs.2011.05.009 %0 Conference Paper %B ICDM Workshops %D 2011 %T Scalable Link Prediction on Multidimensional Networks %A Giulio Rossetti %A Michele Berlingerio %A Fosca Giannotti %B ICDM Workshops %C Vancouver %P 979-986 %0 Conference Paper %B PAKDD (1) %D 2010 %T As Time Goes by: Discovering Eras in Evolving Social Networks %A Michele Berlingerio %A Michele Coscia %A Fosca Giannotti %A Anna Monreale %A Dino Pedreschi %X Within 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. %B PAKDD (1) %P 81-90 %R 10.1007/978-3-642-13657-3_11 %0 Conference Paper %B SEBD %D 2010 %T Discovering Eras in Evolving Social Networks (Extended Abstract) %A Michele Berlingerio %A Michele Coscia %A Fosca Giannotti %A Anna Monreale %A Dino Pedreschi %B SEBD %P 78-85 %0 Conference Paper %B M3SN 2010 Workshop, in conjunction with ICDE2010 %D 2010 %T Towards Discovery of Eras in Social Networks %A Michele Berlingerio %A Michele Coscia %A Fosca Giannotti %A Anna Monreale %A Dino Pedreschi %X In 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. %B M3SN 2010 Workshop, in conjunction with ICDE2010 %R 10.1109/ICDEW.2010.5452713 %0 Book Section %B Biomedical Data and Applications %D 2009 %T Mining Clinical, Immunological, and Genetic Data of Solid Organ Transplantation %A Michele Berlingerio %A Francesco Bonchi %A Michele Curcio %A Fosca Giannotti %A Franco Turini %B Biomedical Data and Applications %P 211-236 %0 Conference Paper %B ECML/PKDD 2009 %D 2009 %T Mining Graph Evolution Rules %A Michele Berlingerio %A Francesco Bonchi %A Björn Bringmann %A Aristides Gionis %B ECML/PKDD 2009 %C Bled, Slovenia %P 115-130 %0 Conference Paper %B SEBD %D 2009 %T Mining the Information Propagation in a Network %A Michele Berlingerio %A Michele Coscia %A Fosca Giannotti %B SEBD %P 333-340 %0 Conference Paper %B SEBD %D 2009 %T Mining the Information Propagation in a Network %A Michele Berlingerio %A Michele Coscia %A Fosca Giannotti %B SEBD %P 333-340 %0 Conference Paper %B IDA %D 2009 %T Mining the Temporal Dimension of the Information Propagation %A Michele Berlingerio %A Michele Coscia %A Fosca Giannotti %B IDA %P 237-248 %0 Conference Paper %B IDA %D 2009 %T Mining the Temporal Dimension of the Information Propagation %A Michele Berlingerio %A Michele Coscia %A Fosca Giannotti %B IDA %P 237-248 %0 Conference Paper %B KDD %D 2009 %T Temporal mining for interactive workflow data analysis %A Michele Berlingerio %A Fabio Pinelli %A Mirco Nanni %A Fosca Giannotti %B KDD %P 109-118 %0 Conference Paper %B SEBD %D 2008 %T Temporal analysis of process logs: a case study %A Michele Berlingerio %A Fosca Giannotti %A Mirco Nanni %A Fabio Pinelli %B SEBD %P 430-437 %G eng %0 Conference Paper %B BIBM %D 2007 %T Mining Clinical Data with a Temporal Dimension: A Case Study %A Michele Berlingerio %A Francesco Bonchi %A Fosca Giannotti %A Franco Turini %B BIBM %P 429-436 %G eng %0 Conference Paper %B ICDM Workshops %D 2007 %T Time-Annotated Sequences for Medical Data Mining %A Michele Berlingerio %A Francesco Bonchi %A Fosca Giannotti %A Franco Turini %B ICDM Workshops %P 133-138 %G eng %0 Conference Paper %B SEBD %D 2007 %T Towards Constraint-Based Subgraph Mining %A Michele Berlingerio %A Francesco Bonchi %A Fosca Giannotti %B SEBD %P 274-281 %G eng %0 Conference Paper %B CBMS %D 2006 %T Mining HLA Patterns Explaining Liver Diseases %A Michele Berlingerio %A Francesco Bonchi %A Silvia Chelazzi %A Michele Curcio %A Fosca Giannotti %A Fabrizio Scatena %B CBMS %P 702-707 %G eng