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2011
Conference article
Open Access
Finding redundant and complementary communities in multidimensional networks
Berlingerio Michele, Coscia Michele, Giannotti Fosca
Community Discovery in networks is the problem of detecting, for each node, its membership to one of more groups of nodes, the communities, that are densely connected, or highly interactive. We de ne this problem for multidimensional networks, i.e. where more than one connection may reside between any two nodes. We introduce two measures able to characterize the communities found. Our experiments on real world data support the methodology proposed, and open the way for a new class of algorithms, aimed at capturing the multifaceted complexity of connections among nodes in a network.Source: 20th ACM international conference on Information and knowledge management, CIKM'11, pp. 2181–2184, Glasgow, UK, 24-28 October 2011
DOI: 10.1145/2063576.2063921
Metrics:
Berlingerio Michele, Coscia Michele, Giannotti Fosca
Community Discovery in networks is the problem of detecting, for each node, its membership to one of more groups of nodes, the communities, that are densely connected, or highly interactive. We de ne this problem for multidimensional networks, i.e. where more than one connection may reside between any two nodes. We introduce two measures able to characterize the communities found. Our experiments on real world data support the methodology proposed, and open the way for a new class of algorithms, aimed at capturing the multifaceted complexity of connections among nodes in a network.Source: 20th ACM international conference on Information and knowledge management, CIKM'11, pp. 2181–2184, Glasgow, UK, 24-28 October 2011
DOI: 10.1145/2063576.2063921
Metrics:
See at:
www.michelecoscia.com 
| dl.acm.org 
| doi.org | CNR ExploRA
2011
Journal article
Open Access
A classification for community discovery methods in complex networks
Coscia M., Giannotti F., Pedreschi D.
Many real-world networks are intimately organized according to a community structure. Much research effort has been devoted to develop methods and algorithms that can efficiently highlight this hidden structure of a network, yielding a vast literature on what is called today community detection. Since network representation can be very complex and can contain different variants in the traditional graph model, each algorithm in the literature focuses on some of these properties and establishes, explicitly or implicitly, its own definition of community. According to this definition, each proposed algorithm then extracts the communities, which typically reflect only part of the features of real communities. The aim of this survey is to provide a 'user manual' for the community discovery problem. Given a meta definition of what a community in a social network is, our aim is to organize the main categories of community discovery methods based on the definition of community they adopt. Given a desired definition of community and the features of a problem (size of network, direction of edges, multidimensionality, and so on) this review paper is designed to provide a set of approaches that researchers could focus on. The proposed classification of community discovery methods is also useful for putting into perspective the many open directions for further research.Source: Statistical analysis and data mining (Online) 4 (2011): 512–546. doi:10.1002/sam.10133
DOI: 10.1002/sam.10133
DOI: 10.48550/arxiv.1206.3552
Metrics:
Coscia M., Giannotti F., Pedreschi D.
Many real-world networks are intimately organized according to a community structure. Much research effort has been devoted to develop methods and algorithms that can efficiently highlight this hidden structure of a network, yielding a vast literature on what is called today community detection. Since network representation can be very complex and can contain different variants in the traditional graph model, each algorithm in the literature focuses on some of these properties and establishes, explicitly or implicitly, its own definition of community. According to this definition, each proposed algorithm then extracts the communities, which typically reflect only part of the features of real communities. The aim of this survey is to provide a 'user manual' for the community discovery problem. Given a meta definition of what a community in a social network is, our aim is to organize the main categories of community discovery methods based on the definition of community they adopt. Given a desired definition of community and the features of a problem (size of network, direction of edges, multidimensionality, and so on) this review paper is designed to provide a set of approaches that researchers could focus on. The proposed classification of community discovery methods is also useful for putting into perspective the many open directions for further research.Source: Statistical analysis and data mining (Online) 4 (2011): 512–546. doi:10.1002/sam.10133
DOI: 10.1002/sam.10133
DOI: 10.48550/arxiv.1206.3552
Metrics:
See at:
arXiv.org e-Print Archive | Statistical Analysis and Data Mining The ASA Data Science Journal | Statistical Analysis and Data Mining The ASA Data Science Journal | doi.org | CNR ExploRA
2011
Conference article
Open Access
Foundations of multidimensional network analysis
Berlingerio Michele, Coscia Michele, Giannotti Fosca, Monreale Anna, Pedreschi Dino
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.Source: 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011, pp. 485–489, Kaohsiung, Taiwan, 25-27 July 2011
DOI: 10.1109/asonam.2011.103
Metrics:
Berlingerio Michele, Coscia Michele, Giannotti Fosca, Monreale Anna, Pedreschi Dino
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.Source: 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011, pp. 485–489, Kaohsiung, Taiwan, 25-27 July 2011
DOI: 10.1109/asonam.2011.103
Metrics:
See at:
www.michelecoscia.com | doi.org | ieeexplore.ieee.org | CNR ExploRA
2011
Journal article
Open Access
The pursuit of hubbiness: analysis of hubs in large multidimensional networks
Berlingerio M., Coscia M., Giannotti F., Monreale A., Pedreschi D.
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.Source: Journal of computational science (Print) 2 (2011): 223–237. doi:10.1016/j.jocs.2011.05.009
DOI: 10.1016/j.jocs.2011.05.009
Metrics:
Berlingerio M., Coscia M., Giannotti F., Monreale A., Pedreschi D.
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.Source: Journal of computational science (Print) 2 (2011): 223–237. doi:10.1016/j.jocs.2011.05.009
DOI: 10.1016/j.jocs.2011.05.009
Metrics:
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Journal of Computational Science | Journal of Computational Science | CNR ExploRA
2011
Conference article
Restricted
Finding and characterizing communities in multidimensional networks
Berlingerio M. Coscia M. Giannotti F.
Complex networks have been receiving increasing attention by the scientific community, also due to the availability of massive network data from diverse domains. One problem studied so far in complex network analysis is Community Discovery, i.e. the detection of group of nodes densely connected, or highly related. However, one aspect of such networks has been disregarded so far: real networks are often multidimensional, i.e. many connections may reside between any two nodes, either to reflect different kinds of relationships, or to connect nodes by different values of the same type of tie. In this context, the problem of Community Discovery has to be redefined, taking into account multidimensionality. In this paper, we attempt to do so, by defining the problem in the multidimensional context, and by introducing also a new measure able to characterize the communities found. We then provide a complete framework for finding and characterizing multidimensional communities. Our experiments on real world multidimensional networks support the methodology proposed in this paper, and open the way for a new class of algorithms, aimed at capturing the multifaceted complexity of connections among nodes in a network.Source: The 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011, pp. 490–494, Kaohsiung, Taiwan, 25-27 July 2011
DOI: 10.1109/asonam.2011.104
Metrics:
Berlingerio M. Coscia M. Giannotti F.
Complex networks have been receiving increasing attention by the scientific community, also due to the availability of massive network data from diverse domains. One problem studied so far in complex network analysis is Community Discovery, i.e. the detection of group of nodes densely connected, or highly related. However, one aspect of such networks has been disregarded so far: real networks are often multidimensional, i.e. many connections may reside between any two nodes, either to reflect different kinds of relationships, or to connect nodes by different values of the same type of tie. In this context, the problem of Community Discovery has to be redefined, taking into account multidimensionality. In this paper, we attempt to do so, by defining the problem in the multidimensional context, and by introducing also a new measure able to characterize the communities found. We then provide a complete framework for finding and characterizing multidimensional communities. Our experiments on real world multidimensional networks support the methodology proposed in this paper, and open the way for a new class of algorithms, aimed at capturing the multifaceted complexity of connections among nodes in a network.Source: The 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011, pp. 490–494, Kaohsiung, Taiwan, 25-27 July 2011
DOI: 10.1109/asonam.2011.104
Metrics:
See at:
doi.org | ieeexplore.ieee.org | CNR ExploRA