Finding redundant and complementary communities in multidimensional networks

Michele Berlingerio; Michele Coscia; Fosca Giannotti

doi:10.1145/2063576.2063921

Finding redundant and complementary communities in multidimensional networks

Michele Berlingerio, Michele Coscia, Fosca Giannotti

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

Résumé

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 define the community discovery problem in multidimensional networks, where more than one connection may reside between any two nodes. We also introduce two measures able to characterize the communities found. 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.

langue originale	Anglais
titre	CIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management
Pages	2181-2184
Nombre de pages	4
Les DOIs	https://doi.org/10.1145/2063576.2063921
Etat de la publication	Publié - 2011
Modification externe	Oui
Evénement	20th ACM Conference on Information and Knowledge Management, CIKM'11 - Glasgow, Royaume-Uni Durée: 24 oct. 2011 → 28 oct. 2011

Une conférence

Une conférence	20th ACM Conference on Information and Knowledge Management, CIKM'11
Pays/Territoire	Royaume-Uni
La ville	Glasgow
période	24/10/11 → 28/10/11

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10.1145/2063576.2063921

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Link to publication in Scopus

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title = "Finding redundant and complementary communities in multidimensional networks",

abstract = "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 define the community discovery problem in multidimensional networks, where more than one connection may reside between any two nodes. We also introduce two measures able to characterize the communities found. 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.",

keywords = "community discovery, multidimensional network analysis",

author = "Michele Berlingerio and Michele Coscia and Fosca Giannotti",

year = "2011",

doi = "10.1145/2063576.2063921",

language = "English",

isbn = "9781450307178",

pages = "2181--2184",

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Berlingerio, M, Coscia, M & Giannotti, F 2011, Finding redundant and complementary communities in multidimensional networks. Dans CIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management. p. 2181-2184, 20th ACM Conference on Information and Knowledge Management, CIKM'11, Glasgow, Royaume-Uni, 24/10/11. https://doi.org/10.1145/2063576.2063921

Finding redundant and complementary communities in multidimensional networks. / Berlingerio, Michele; Coscia, Michele; Giannotti, Fosca.
CIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management. 2011. p. 2181-2184.

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

TY - GEN

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AU - Berlingerio, Michele

AU - Coscia, Michele

AU - Giannotti, Fosca

PY - 2011

Y1 - 2011

N2 - 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 define the community discovery problem in multidimensional networks, where more than one connection may reside between any two nodes. We also introduce two measures able to characterize the communities found. 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.

AB - 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 define the community discovery problem in multidimensional networks, where more than one connection may reside between any two nodes. We also introduce two measures able to characterize the communities found. 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.

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KW - multidimensional network analysis

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U2 - 10.1145/2063576.2063921

DO - 10.1145/2063576.2063921

M3 - Conference contribution

AN - SCOPUS:83055179251

SN - 9781450307178

SP - 2181

EP - 2184

BT - CIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management

T2 - 20th ACM Conference on Information and Knowledge Management, CIKM'11

Y2 - 24 October 2011 through 28 October 2011

ER -

Finding redundant and complementary communities in multidimensional networks

Résumé

Une conférence

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