@conference {1127,
	title = {On the Equivalence Between Community Discovery and Clustering},
	booktitle = {International Conference on Smart Objects and Technologies for Social Good},
	year = {2017},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	author = {Riccardo Guidotti and Michele Coscia}
}
@inbook {817,
	title = {Going Beyond GDP to Nowcast Well-Being Using Retail Market Data},
	booktitle = {Advances in Network Science},
	year = {2016},
	pages = {29{\textendash}42},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {One of the most used measures of the economic health of a nation is the Gross Domestic Product (GDP): the market value of all officially recognized final goods and services produced within a country in a given period of time. GDP, prosperity and well-being of the citizens of a country have been shown to be highly correlated. However, GDP is an imperfect measure in many respects. GDP usually takes a lot of time to be estimated and arguably the well-being of the people is not quantifiable simply by the market value of the products available to them. In this paper we use a quantification of the average sophistication of satisfied needs of a population as an alternative to GDP. We show that this quantification can be calculated more easily than GDP and it is a very promising predictor of the GDP value, anticipating its estimation by six months. The measure is arguably a more multifaceted evaluation of the well-being of the population, as it tells us more about how people are satisfying their needs. Our study is based on a large dataset of retail micro transactions happening across the Italian territory.},
	doi = {10.1007/978-3-319-28361-6_3},
	author = {Riccardo Guidotti and Michele Coscia and Dino Pedreschi and Diego Pennacchioli}
}
@conference {763,
	title = {Behavioral Entropy and Profitability in Retail},
	booktitle = {IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA{\textquoteright}2015)},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Paris},
	abstract = {Human behavior is predictable in principle: people are systematic in their everyday choices. This predictability can be used to plan events and infrastructure, both for the public good and for private gains. In this paper we investigate the largely unexplored relationship between the systematic behavior of a customer and its profitability for a retail company. We estimate a customer{\textquoteright}s behavioral entropy over two dimensions: the basket entropy is the variety of what customers buy, and the spatio-temporal entropy is the spatial and temporal variety of their shopping sessions. To estimate the basket and the spatiotemporal entropy we use data mining and information theoretic techniques. We find that predictable systematic customers are more profitable for a supermarket: their average per capita expenditures are higher than non systematic customers and they visit the shops more often. However, this higher individual profitability is masked by its overall level. The highly systematic customers are a minority of the customer set. As a consequence, the total amount of revenues they generate is small. We suggest that favoring a systematic behavior in their customers might be a good strategy for supermarkets to increase revenue. These results are based on data coming from a large Italian supermarket chain, including more than 50 thousand customers visiting 23 shops to purchase more than 80 thousand distinct products.},
	author = {Riccardo Guidotti and Michele Coscia and Dino Pedreschi and Diego Pennacchioli}
}
@article {752,
	title = {Product assortment and customer mobility},
	journal = {EPJ Data Science},
	volume = {4},
	number = {1},
	year = {2015},
	month = {10-2015},
	pages = {1{\textendash}18},
	abstract = {Customers mobility is dependent on the sophistication of their needs: sophisticated customers need to travel more to fulfill their needs. In this paper, we provide more detailed evidence of this phenomenon, providing an empirical validation of the Central Place Theory. For each customer, we detect what is her favorite shop, where she purchases most products. We can study the relationship between the favorite shop and the closest one, by recording the influence of the shop{\textquoteright}s size and the customer{\textquoteright}s sophistication in the discordance cases, i.e. the cases in which the favorite shop is not the closest one. We show that larger shops are able to retain most of their closest customers and they are able to catch large portions of customers from smaller shops around them. We connect this observation with the shop{\textquoteright}s larger sophistication, and not with its other characteristics, as the phenomenon is especially noticeable when customers want to satisfy their sophisticated needs. This is a confirmation of the recent extensions of the Central Place Theory, where the original assumptions of homogeneity in customer purchase power and needs are challenged. Different types of shops have also different survival logics. The largest shops get closed if they are unable to catch customers from the smaller shops, while medium size shops get closed if they cannot retain their closest customers. All analysis are performed on a large real-world dataset recording all purchases from millions of customers across the west coast of Italy.},
	doi = {10.1140/epjds/s13688-015-0051-3},
	url = {http://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-015-0051-3},
	author = {Michele Coscia and Diego Pennacchioli and Fosca Giannotti}
}
@conference {827,
	title = {Overlap versus partition: marketing classification and customer profiling in complex networks of products},
	booktitle = {Data engineering workshops (ICDEW), 2014 IEEE 30th international conference on},
	year = {2014},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In recent years we witnessed the explosion in the availability of data regarding human and customer behavior in the market. This data richness era has fostered the development of useful applications in understanding how markets and the minds of the customers work. In this paper we focus on the analysis of complex networks based on customer behavior. Complex network analysis has provided a new and wide toolbox for the classic data mining task of clustering. With community discovery, i.e. the detection of functional modules in complex networks, we are now able to group together customers and products using a variety of different criteria. The aim of this paper is to explore this new analytic degree of freedom. We are interested in providing a case study uncovering the meaning of different community discovery algorithms on a network of products connected together because co-purchased by the same customers. We focus our interest in the different interpretation of a partition approach, where each product belongs to a single community, against an overlapping approach, where each product can belong to multiple communities. We found that the former is useful to improve the marketing classification of products, while the latter is able to create a collection of different customer profiles.},
	doi = {10.1109/ICDEW.2014.6818312},
	url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6818312},
	author = {Diego Pennacchioli and Michele Coscia and Dino Pedreschi}
}
@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 {828,
	title = {The retail market as a complex system},
	journal = {EPJ Data Science},
	volume = {3},
	number = {1},
	year = {2014},
	pages = {1{\textendash}27},
	abstract = {Aim of this paper is to introduce the complex system perspective into retail market analysis. Currently, to understand the retail market means to search for local patterns at the micro level, involving the segmentation, separation and profiling of diverse groups of consumers. In other contexts, however, markets are modelled as complex systems. Such strategy is able to uncover emerging regularities and patterns that make markets more predictable, e.g. enabling to predict how much a country{\textquoteright}s GDP will grow. Rather than isolate actors in homogeneous groups, this strategy requires to consider the system as a whole, as the emerging pattern can be detected only as a result of the interaction between its self-organizing parts. This assumption holds also in the retail market: each customer can be seen as an independent unit maximizing its own utility function. As a consequence, the global behaviour of the retail market naturally emerges, enabling a novel description of its properties, complementary to the local pattern approach. Such task demands for a data-driven empirical framework. In this paper, we analyse a unique transaction database, recording the micro-purchases of a million customers observed for several years in the stores of a national supermarket chain. We show the emergence of the fundamental pattern of this complex system, connecting the products{\textquoteright} volumes of sales with the customers{\textquoteright} volumes of purchases. This pattern has a number of applications. We provide three of them. By enabling us to evaluate the sophistication of needs that a customer has and a product satisfies, this pattern has been applied to the task of uncovering the hierarchy of needs of the customers, providing a hint about what is the next product a customer could be interested in buying and predicting in which shop she is likely to go to buy it.},
	doi = {10.1140/epjds/s13688-014-0033-x},
	url = {http://link.springer.com/article/10.1140/epjds/s13688-014-0033-x},
	author = {Diego Pennacchioli and Michele Coscia and S Rinzivillo and Fosca Giannotti and Dino Pedreschi}
}
@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}
}
@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 {529,
	title = {Explaining the PRoduct Range Effect in Purchase Data},
	year = {2013},
	author = {Diego Pennacchioli and Michele Coscia and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@article {527,
	title = {Spatial and Temporal Evaluation of Network-based Analysis of Human Mobility},
	journal = {Social Network Analysis and Mining},
	volume = {to appear},
	year = {2013},
	author = {Michele Coscia and S Rinzivillo and Fosca Giannotti and Dino Pedreschi}
}
@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 {570,
	title = {Classifying Trust/Distrust Relationships in Online Social Networks},
	booktitle = {2012 International Conference on Privacy, Security, Risk and Trust, {PASSAT} 2012, and 2012 International Confernece on Social Computing, SocialCom 2012, Amsterdam, Netherlands, September 3-5, 2012},
	year = {2012},
	pages = {552{\textendash}557},
	abstract = {Online social networks are increasingly being used as places where communities gather to exchange information, form opinions, collaborate in response to events. An aspect of this information exchange is how to determine if a source of social information can be trusted or not. Data mining literature addresses this problem. However, if usually employs social balance theories, by looking at small structures in complex networks known as triangles. This has proven effective in some cases, but it under performs in the lack of context information about the relation and in more complex interactive structures. In this paper we address the problem of creating a framework for the trust inference, able to infer the trust/distrust relationships in those relational environments that cannot be described by using the classical social balance theory. We do so by decomposing a trust network in its ego network components and mining on this ego network set the trust relationships, extending a well known graph mining algorithm. We test our framework on three public datasets describing trust relationships in the real world (from the social media Epinions, Slash dot and Wikipedia) and confronting our results with the trust inference state of the art, showing better performances where the social balance theory fails.},
	doi = {10.1109/SocialCom-PASSAT.2012.115},
	url = {http://dx.doi.org/10.1109/SocialCom-PASSAT.2012.115},
	author = {Giacomo Bachi and Michele Coscia and Anna Monreale 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}
}
@article {455,
	title = {Discovering the Geographical Borders of Human Mobility},
	journal = {KI - K{\"u}nstliche Intelligenz},
	year = {2012},
	chapter = {1},
	abstract = {The availability of massive network and mobility data from diverse domains has fostered the analysis of human behavior and interactions. Broad, extensive, and multidisciplinary research has been devoted to the extraction of non-trivial knowledge from this novel form of data. We propose a general method to determine the influence of social and mobility behavior over a specific geographical area in order to evaluate to what extent the current administrative borders represent the real basin of human movement. We build a network representation of human movement starting with vehicle GPS tracks and extract relevant clusters, which are then mapped back onto the territory, finding a good match with the existing administrative borders. The novelty of our approach is the focus on a detailed spatial resolution, we map emerging borders in terms of individual municipalities, rather than macro regional or national areas. We present a series of experiments to illustrate and evaluate the effectiveness of our approach.},
	issn = {0933-1875},
	doi = {10.1007/s13218-012-0181-8},
	url = {https://link.springer.com/article/10.1007\%2Fs13218-012-0181-8},
	author = {S Rinzivillo and Simone Mainardi and Fabio Pezzoni and Michele Coscia and Fosca Giannotti and Dino Pedreschi}
}
@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}
}
@proceedings {461,
	title = {Optimal Spatial Resolution for the Analysis of Human Mobility},
	year = {2012},
	address = {Instanbul, Turkey},
	author = {Michele Coscia and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@article {sam2011,
	title = {A classification for community discovery methods in complex networks},
	journal = {Statistical Analysis and Data Mining},
	volume = {4},
	number = {5},
	year = {2011},
	pages = {512-546},
	author = {Michele Coscia and Fosca Giannotti 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}
}
@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 {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}
}
@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 {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 {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 {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 {asonam09,
	title = {Social Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography},
	booktitle = {ASONAM},
	year = {2009},
	pages = {279-283},
	author = {Michele Coscia and Fosca Giannotti and Ruggero G. Pensa}
}
@conference {CosciaGP09,
	title = {Social Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography},
	booktitle = {ASONAM},
	year = {2009},
	pages = {279-283},
	author = {Michele Coscia and Fosca Giannotti and Ruggero G. Pensa}
}
