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Local Community Detection Based on Small Cliques

Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany
Author to whom correspondence should be addressed.
Algorithms 2017, 10(3), 90;
Received: 21 June 2017 / Revised: 3 August 2017 / Accepted: 4 August 2017 / Published: 11 August 2017
(This article belongs to the Special Issue Algorithms for Community Detection in Complex Networks)
Community detection aims to find dense subgraphs in a network. We consider the problem of finding a community locally around a seed node both in unweighted and weighted networks. This is a faster alternative to algorithms that detect communities that cover the whole network when actually only a single community is required. Further, many overlapping community detection algorithms use local community detection algorithms as basic building block. We provide a broad comparison of different existing strategies of expanding a seed node greedily into a community. For this, we conduct an extensive experimental evaluation both on synthetic benchmark graphs as well as real world networks. We show that results both on synthetic as well as real-world networks can be significantly improved by starting from the largest clique in the neighborhood of the seed node. Further, our experiments indicate that algorithms using scores based on triangles outperform other algorithms in most cases. We provide theoretical descriptions as well as open source implementations of all algorithms used. View Full-Text
Keywords: community detection; local community; clique; iterative expansion community detection; local community; clique; iterative expansion
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MDPI and ACS Style

Hamann, M.; Röhrs, E.; Wagner, D. Local Community Detection Based on Small Cliques. Algorithms 2017, 10, 90.

AMA Style

Hamann M, Röhrs E, Wagner D. Local Community Detection Based on Small Cliques. Algorithms. 2017; 10(3):90.

Chicago/Turabian Style

Hamann, Michael, Eike Röhrs, and Dorothea Wagner. 2017. "Local Community Detection Based on Small Cliques" Algorithms 10, no. 3: 90.

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