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

Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	CIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management
Pages	2181-2184
Number of pages	4
DOIs	https://doi.org/10.1145/2063576.2063921
Publication status	Published - 2011
Externally published	Yes
Event	20th ACM Conference on Information and Knowledge Management, CIKM'11 - Glasgow, United Kingdom Duration: 24 Oct 2011 → 28 Oct 2011

Conference

Conference	20th ACM Conference on Information and Knowledge Management, CIKM'11
Country/Territory	United Kingdom
City	Glasgow
Period	24/10/11 → 28/10/11

Keywords

community discovery
multidimensional network analysis

Access to Document

10.1145/2063576.2063921

Cite this

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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",

booktitle = "CIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management",

note = "20th ACM Conference on Information and Knowledge Management, CIKM'11 ; Conference date: 24-10-2011 Through 28-10-2011",

}

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

TY - GEN

T1 - Finding redundant and complementary communities in multidimensional networks

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.

KW - community discovery

KW - multidimensional network analysis

UR - http://www.scopus.com/inward/record.url?scp=83055179251&partnerID=8YFLogxK

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

Abstract

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Keywords

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