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Journal article · Preprint article

Biclique communities

By Jørgensen, Sune Lehmann1,2; Hansen-Schwartz, Martin1; Hansen, Lars Kai1,2

From

Department of Informatics and Mathematical Modeling, Technical University of Denmark1

Cognitive Systems, Department of Informatics and Mathematical Modeling, Technical University of Denmark2

We present a method for detecting communities in bipartite networks. Based on an extension of the k-clique community detection algorithm, we demonstrate how modular structure in bipartite networks presents itself as overlapping bicliques. If bipartite information is available, the biclique community detection algorithm retains all of the advantages of the k-clique algorithm, but avoids discarding important structural information when performing a one-mode projection of the network.

Further, the biclique community detection algorithm provides a level of flexibility by incorporating independent clique thresholds for each of the nonoverlapping node sets in the bipartite network

Language: English
Year: 2008
Pages: 016108
ISSN: 24700053 , 24700045 , 15502376 and 15393755
Types: Journal article and Preprint article
DOI: 10.1103/PhysRevE.78.016108
ORCIDs: Jørgensen, Sune Lehmann and Hansen, Lars Kai
Keywords

evolution networks

Other keywords

physics.data-an physics.soc-ph

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