@inbook {818,
	title = {Where Is My Next Friend? Recommending Enjoyable Profiles in Location Based Services},
	booktitle = {Complex Networks VII},
	year = {2016},
	pages = {65{\textendash}78},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {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{\textquoteright} 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.},
	doi = {10.1007/978-3-319-30569-1_5},
	author = {Riccardo Guidotti and Michele Berlingerio}
}
@conference {764,
	title = {Mobility Mining for Journey Planning in Rome},
	booktitle = {Machine Learning and Knowledge Discovery in Databases},
	year = {2015},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {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 {\textquotedblleft}bus lines{\textquotedblright}, 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.},
	author = {Michele Berlingerio and Bicer, Veli and Botea, Adi and Braghin, Stefano and Lopes, Nuno and Riccardo Guidotti and Francesca Pratesi}
}
@conference {823,
	title = {Social or green? A data-driven approach for more enjoyable carpooling},
	booktitle = {Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Riccardo Guidotti and Sassi, Andrea and Michele Berlingerio and Pascale, Alessandra and Ghaddar, Bissan}
}
@article {567,
	title = {Evolving networks: Eras and turning points},
	journal = {Intell. Data Anal.},
	volume = {17},
	number = {1},
	year = {2013},
	pages = {27{\textendash}48},
	abstract = {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.},
	doi = {10.3233/IDA-120566},
	url = {http://dx.doi.org/10.3233/IDA-120566},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@proceedings {545,
	title = {ComeTogether: Discovering Communities of Places in Mobility Data},
	year = {2012},
	month = {2012},
	pages = { 268-273},
	author = {Igo Brilhante and Michele Berlingerio and Roberto Trasarti and Chiara Renso and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Marco A. Casanova}
}
@article {482,
	title = {Multidimensional networks: foundations of structural analysis},
	journal = {World Wide Web},
	volume = { Volume 15 / 2012},
	year = {2012},
	month = {10/2012},
	abstract = {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. },
	doi = {10.1007/s11280-012-0190-4},
	url = {http://www.springerlink.com/content/f774289854430410/abstract/},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@conference {asonam22011,
	title = {Finding and Characterizing Communities in Multidimensional Networks},
	booktitle = {ASONAM},
	year = {2011},
	pages = {490-494},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {cikm2011,
	title = {Finding redundant and complementary communities in multidimensional networks},
	booktitle = {CIKM},
	year = {2011},
	pages = {2181-2184},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {asonam12011,
	title = {Foundations of Multidimensional Network Analysis},
	booktitle = {ASONAM},
	year = {2011},
	pages = {485-489},
	abstract = {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.},
	doi = {10.1109/ASONAM.2011.103},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale 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}
}
@article {jocs2011,
	title = {The pursuit of hubbiness: Analysis of hubs in large multidimensional networks},
	journal = {J. Comput. Science},
	volume = {2},
	number = {3},
	year = {2011},
	pages = {223-237},
	abstract = {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{\textendash}word query log network, outlier detection in a social network, and temporal analysis of behaviors in a co-authorship network.},
	doi = {10.1016/j.jocs.2011.05.009},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@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}
}
@conference {pakdd2010,
	title = {As Time Goes by: Discovering Eras in Evolving Social Networks},
	booktitle = {PAKDD (1)},
	year = {2010},
	pages = {81-90},
	abstract = {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.},
	doi = {10.1007/978-3-642-13657-3_11},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@conference {sebd10,
	title = {Discovering Eras in Evolving Social Networks (Extended Abstract)},
	booktitle = {SEBD},
	year = {2010},
	pages = {78-85},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@conference {Berlm3sn2010,
	title = {Towards Discovery of Eras in Social Networks},
	booktitle = {M3SN 2010 Workshop, in conjunction with ICDE2010},
	year = {2010},
	abstract = {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.},
	doi = {10.1109/ICDEW.2010.5452713},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@inbook {BerlingerioBCGT9,
	title = {Mining Clinical, Immunological, and Genetic Data of Solid Organ Transplantation},
	booktitle = {Biomedical Data and Applications},
	year = {2009},
	pages = {211-236},
	author = {Michele Berlingerio and Francesco Bonchi and Michele Curcio and Fosca Giannotti and Franco Turini}
}
@conference {BerlingerioBBG09,
	title = {Mining Graph Evolution Rules},
	booktitle = {ECML/PKDD 2009},
	year = {2009},
	pages = {115-130},
	address = {Bled, Slovenia},
	author = {Michele Berlingerio and Francesco Bonchi and Bj{\"o}rn Bringmann and Aristides Gionis}
}
@conference {BerlingerioCG09,
	title = {Mining the Information Propagation in a Network},
	booktitle = {SEBD},
	year = {2009},
	pages = {333-340},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {sebd09,
	title = {Mining the Information Propagation in a Network},
	booktitle = {SEBD},
	year = {2009},
	pages = {333-340},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {ida09,
	title = {Mining the Temporal Dimension of the Information Propagation},
	booktitle = {IDA},
	year = {2009},
	pages = {237-248},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {BerlingerioCG09,
	title = {Mining the Temporal Dimension of the Information Propagation},
	booktitle = {IDA},
	year = {2009},
	pages = {237-248},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {BerlingerioPNG09,
	title = {Temporal mining for interactive workflow data analysis},
	booktitle = {KDD},
	year = {2009},
	pages = {109-118},
	author = {Michele Berlingerio and Fabio Pinelli and Mirco Nanni and Fosca Giannotti}
}
@conference {DBLP:conf/sebd/BerlingerioGNP08,
	title = {Temporal analysis of process logs: a case study},
	booktitle = {SEBD},
	year = {2008},
	pages = {430-437},
	author = {Michele Berlingerio and Fosca Giannotti and Mirco Nanni and Fabio Pinelli}
}
@conference {DBLP:conf/bibm/BerlingerioBGT07,
	title = {Mining Clinical Data with a Temporal Dimension: A Case Study},
	booktitle = {BIBM},
	year = {2007},
	pages = {429-436},
	author = {Michele Berlingerio and Francesco Bonchi and Fosca Giannotti and Franco Turini}
}
@conference {DBLP:conf/icdm/BerlingerioBGT07,
	title = {Time-Annotated Sequences for Medical Data Mining},
	booktitle = {ICDM Workshops},
	year = {2007},
	pages = {133-138},
	author = {Michele Berlingerio and Francesco Bonchi and Fosca Giannotti and Franco Turini}
}
@conference {DBLP:conf/sebd/BerlingerioBG07,
	title = {Towards Constraint-Based Subgraph Mining},
	booktitle = {SEBD},
	year = {2007},
	pages = {274-281},
	author = {Michele Berlingerio and Francesco Bonchi and Fosca Giannotti}
}
@conference {DBLP:conf/cbms/BerlingerioBCCGS06,
	title = {Mining HLA Patterns Explaining Liver Diseases},
	booktitle = {CBMS},
	year = {2006},
	pages = {702-707},
	author = {Michele Berlingerio and Francesco Bonchi and Silvia Chelazzi and Michele Curcio and Fosca Giannotti and Fabrizio Scatena}
}
