00434nas a2200109   4500008004100000245006600041210005900107260001900166100002300185700002000208856009600228       2017                        eng d00aOn the Equivalence Between Community Discovery and Clustering0 aEquivalence Between Community Discovery and Clustering  bSpringer, Cham1 aGuidotti, Riccardo1 aCoscia, Michele  uhttps://kdd.isti.cnr.it/publications/equivalence-between-community-discovery-and-clustering01694nas a2200157   4500008004100000245006800041210006700109260003800176300001200214520112100226100002301347700002001370700002001390700002401410856010201434       2016                        eng d00aGoing Beyond GDP to Nowcast Well-Being Using Retail Market Data0 aGoing Beyond GDP to Nowcast WellBeing Using Retail Market Data  bSpringer International Publishing  a29–423 aOne 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.1 aGuidotti, Riccardo1 aCoscia, Michele1 aPedreschi, Dino1 aPennacchioli, Diego  uhttps://kdd.isti.cnr.it/publications/going-beyond-gdp-nowcast-well-being-using-retail-market-data01914nas a2200145   4500008004100000245005100041210005100092260001600143520143700159100002301596700002001619700002001639700002401659856008501683       2015                        eng d00aBehavioral Entropy and Profitability in Retail0 aBehavioral Entropy and Profitability in Retail  aParisbIEEE3 aHuman 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’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.1 aGuidotti, Riccardo1 aCoscia, Michele1 aPedreschi, Dino1 aPennacchioli, Diego  uhttps://kdd.isti.cnr.it/publications/behavioral-entropy-and-profitability-retail02010nas a2200157   4500008004100000245004500041210004500086260001200131300001100143490000600154520154300160100002001703700002401723700002101747856008401768       2015                        eng d00aProduct assortment and customer mobility0 aProduct assortment and customer mobility  c10-2015  a1–180 v43 aCustomers 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’s size and the customer’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’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.1 aCoscia, Michele1 aPennacchioli, Diego1 aGiannotti, Fosca  uhttp://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-015-0051-301798nas a2200133   4500008004100000245011000041210006900151260000900220520130600229100002401535700002001559700002001579856006501599       2014                        eng d00aOverlap versus partition: marketing classification and customer profiling in complex networks of products0 aOverlap versus partition marketing classification and customer p  bIEEE3 aIn 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.1 aPennacchioli, Diego1 aCoscia, Michele1 aPedreschi, Dino  uhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=681831200482nas a2200145   4500008004100000245006800041210006300109100002400172700002100196700002100217700002000238700002100258700002000279856003700299       2014                        eng d00aThe patterns of musical influence on the Last.Fm social network0 apatterns of musical influence on the LastFm social network1 aPennacchioli, Diego1 aRossetti, Giulio1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca1 aCoscia, Michele  uhttps://kdd.isti.cnr.it/node/62302340nas a2200169   4500008004100000245004200041210003800083300001100121490000600132520186000138100002401998700002002022700001802042700002102060700002002081856006902101       2014                        eng d00aThe retail market as a complex system0 aretail market as a complex system  a1–270 v33 aAim 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’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’ volumes of sales with the customers’ 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.1 aPennacchioli, Diego1 aCoscia, Michele1 aRinzivillo, S1 aGiannotti, Fosca1 aPedreschi, Dino  uhttp://link.springer.com/article/10.1140/epjds/s13688-014-0033-x02438nas a2200157   4500008004100000245008400041210006900125300000600194490000600200520195300206100002002159700002102179700002102200700002002221856003902241       2014                        eng d00aUncovering Hierarchical and Overlapping Communities with a Local-First Approach0 aUncovering Hierarchical and Overlapping Communities with a Local  a60 v93 aCommunity discovery in complex networks is the task of organizing a network’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.1 aCoscia, Michele1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttp://doi.acm.org/10.1145/262951101933nas a2200169   4500008003900000245004700039210004600086300001200132490000700144520146600151100002501617700002001642700002101662700001901683700002001702856004101722       2013                          d00aEvolving networks: Eras and turning points0 aEvolving networks Eras and turning points  a27–480 v173 aWithin 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.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttp://dx.doi.org/10.3233/IDA-12056600477nas a2200133   4500008003900000245005700039210005700096100002400153700002000177700001800197700002000215700002100235856008700256       2013                          d00aExplaining the PRoduct Range Effect in Purchase Data0 aExplaining the PRoduct Range Effect in Purchase Data1 aPennacchioli, Diego1 aCoscia, Michele1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/explaining-product-range-effect-purchase-data00526nas a2200133   4500008003900000245008000039210006900119490001400188100002000202700001800222700002100240700002000261856011100281       2013                          d00aSpatial and Temporal Evaluation of Network-based Analysis of Human Mobility0 aSpatial and Temporal Evaluation of Networkbased Analysis of Huma0 vto appear1 aCoscia, Michele1 aRinzivillo, S1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/spatial-and-temporal-evaluation-network-based-analysis-human-mobility00453nas a2200145   4500008004100000245004600041210004200087100002400129700002100153700002100174700002000195700002100215700002000236856005100256       2013                        eng d00aThe Three Dimensions of Social Prominence0 aThree Dimensions of Social Prominence1 aPennacchioli, Diego1 aRossetti, Giulio1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca1 aCoscia, Michele  uhttp://dx.doi.org/10.1007/978-3-319-03260-3_2800579nas a2200133   4500008003900000245009500039210006900134100002000203700002100223700002400244700002400268700002100292856013200313       2013                          d00aYou Know Because I Know”: a Multidimensional Network Approach to Human Resources Problem0 aYou Know Because I Know a Multidimensional Network Approach to H1 aCoscia, Michele1 aRossetti, Giulio1 aPennacchioli, Diego1 aCeccarelli, Damiano1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/you-know-because-i-know%E2%80%9D-multidimensional-network-approach-human-resources-problem01793nas a2200145   4500008003900000245007100039210006900110300001400179520131900193100001901512700002001531700001901551700002101570856005601591       2012                          d00aClassifying Trust/Distrust Relationships in Online Social Networks0 aClassifying TrustDistrust Relationships in Online Social Network  a552–5573 aOnline 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.1 aBachi, Giacomo1 aCoscia, Michele1 aMonreale, Anna1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1109/SocialCom-PASSAT.2012.11500430nas a2200121   4500008004100000245007000041210006800111100002000179700002100199700002100220700002000241856004700261       2012                        eng d00aDEMON: a local-first discovery method for overlapping communities0 aDEMON a localfirst discovery method for overlapping communities1 aCoscia, Michele1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttp://doi.acm.org/10.1145/2339530.233963000502nas a2200133   4500008003900000245007000039210006800109260000900177100002000186700002100206700002100227700002000248856010000268       2012                          d00aDEMON: a Local-First Discovery Method for Overlapping Communities0 aDEMON a LocalFirst Discovery Method for Overlapping Communities  c20121 aCoscia, Michele1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/demon-local-first-discovery-method-overlapping-communities01549nas a2200169   4500008003900000022001400039245005900053210005900112520102300171100001801194700002101212700001901233700002001252700002101272700002001293856006601313       2012                          d  a0933-187500aDiscovering the Geographical Borders of Human Mobility0 aDiscovering the Geographical Borders of Human Mobility3 aThe 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.1 aRinzivillo, S1 aMainardi, Simone1 aPezzoni, Fabio1 aCoscia, Michele1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/article/10.1007%2Fs13218-012-0181-802055nas a2200169   4500008003900000245006600039210006500105260001200170490002200182520150900204100002501713700002001738700002101758700001901779700002001798856006701818       2012                          d00aMultidimensional networks: foundations of structural analysis0 aMultidimensional networks foundations of structural analysis  c10/20120 v Volume 15 / 20123 aComplex 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. 1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttp://www.springerlink.com/content/f774289854430410/abstract/00498nas a2200133   4500008003900000245006600039210006600105260002200171100002000193700001800213700002000231700002100251856009200272       2012                          d00aOptimal Spatial Resolution for the Analysis of Human Mobility0 aOptimal Spatial Resolution for the Analysis of Human Mobility  aInstanbul, Turkey1 aCoscia, Michele1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/optimal-spatial-resolution-analysis-human-mobility00495nas a2200133   4500008003900000245007300039210006900112300001200181490000600193100002000199700002100219700002000240856010100260       2011                          d00aA classification for community discovery methods in complex networks0 aclassification for community discovery methods in complex networ  a512-5460 v41 aCoscia, Michele1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/classification-community-discovery-methods-complex-networks00486nas a2200121   4500008003900000245007200039210006900111300001200180100002500192700002000217700002100237856010600258       2011                          d00aFinding and Characterizing Communities in Multidimensional Networks0 aFinding and Characterizing Communities in Multidimensional Netwo  a490-4941 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/finding-and-characterizing-communities-multidimensional-networks00506nas a2200121   4500008003900000245008100039210006900120300001400189100002500203700002000228700002100248856011500269       2011                          d00aFinding redundant and complementary communities in multidimensional networks0 aFinding redundant and complementary communities in multidimensio  a2181-21841 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/finding-redundant-and-complementary-communities-multidimensional-networks01804nas a2200157   4500008003900000245005300039210005300092300001200145520129700157100002501454700002001479700002101499700001901520700002001539856008701559       2011                          d00aFoundations of Multidimensional Network Analysis0 aFoundations of Multidimensional Network Analysis  a485-4893 aComplex 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.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/foundations-multidimensional-network-analysis01999nas a2200169   4500008003900000245008200039210006900121300001200190490000600202520141100208100002501619700002001644700002101664700001901685700002001704856010501724       2011                          d00aThe pursuit of hubbiness: Analysis of hubs in large multidimensional networks0 apursuit of hubbiness Analysis of hubs in large multidimensional   a223-2370 v23 aHubs 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.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/pursuit-hubbiness-analysis-hubs-large-multidimensional-networks01803nas a2200157   4500008003900000245006600039210006500105300001000170520126700180100002501447700002001472700002101492700001901513700002001532856009301552       2010                          d00aAs Time Goes by: Discovering Eras in Evolving Social Networks0 aAs Time Goes by Discovering Eras in Evolving Social Networks  a81-903 aWithin 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.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/time-goes-discovering-eras-evolving-social-networks00537nas a2200145   4500008003900000245006900039210006700108300001000175100002500185700002000210700002100230700001900251700002000270856010100290       2010                          d00aDiscovering Eras in Evolving Social Networks (Extended Abstract)0 aDiscovering Eras in Evolving Social Networks Extended Abstract  a78-851 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/discovering-eras-evolving-social-networks-extended-abstract01363nas a2200145   4500008003900000245004900039210004900088520089500137100002501032700002001057700002101077700001901098700002001117856008001137       2010                          d00aTowards Discovery of Eras in Social Networks0 aTowards Discovery of Eras in Social Networks3 aIn 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.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/towards-discovery-eras-social-networks00425nas a2200121   4500008003900000245005200039210005200091300001200143100002500155700002000180700002100200856008200221       2009                          d00aMining the Information Propagation in a Network0 aMining the Information Propagation in a Network  a333-3401 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mining-information-propagation-network-000423nas a2200121   4500008003900000245005200039210005200091300001200143100002500155700002000180700002100200856008000221       2009                          d00aMining the Information Propagation in a Network0 aMining the Information Propagation in a Network  a333-3401 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mining-information-propagation-network00455nas a2200121   4500008003900000245006500039210006500104300001200169100002500181700002000206700002100226856008600247       2009                          d00aMining the Temporal Dimension of the Information Propagation0 aMining the Temporal Dimension of the Information Propagation  a237-2481 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/mining-temporal-dimension-information-propagation00460nas a2200121   4500008003900000245006500039210006500104300001200169100002500181700002000206700002100226856009100247       2009                          d00aMining the Temporal Dimension of the Information Propagation0 aMining the Temporal Dimension of the Information Propagation  a237-2481 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mining-temporal-dimension-information-propagation00529nas a2200121   4500008003900000245009700039210006900136300001200205100002000217700002100237700002200258856012700280       2009                          d00aSocial Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography0 aSocial Network Analysis as Knowledge Discovery Process A Case St  a279-2831 aCoscia, Michele1 aGiannotti, Fosca1 aPensa, Ruggero, G  uhttps://kdd.isti.cnr.it/publications/social-network-analysis-knowledge-discovery-process-case-study-digital-bibliography-000527nas a2200121   4500008003900000245009700039210006900136300001200205100002000217700002100237700002200258856012500280       2009                          d00aSocial Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography0 aSocial Network Analysis as Knowledge Discovery Process A Case St  a279-2831 aCoscia, Michele1 aGiannotti, Fosca1 aPensa, Ruggero, G  uhttps://kdd.isti.cnr.it/publications/social-network-analysis-knowledge-discovery-process-case-study-digital-bibliography