Physics – Data Analysis – Statistics and Probability
Scientific paper
2007-10-25
Phys. Rev. E, v78, p016108 (2008)
Physics
Data Analysis, Statistics and Probability
10 pages, 6 figures
Scientific paper
10.1103/PhysRevE.78.016108
We present a novel 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 bi-clique 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 bi-clique community detection algorithm provides a new level of flexibility by incorporating independent clique thresholds for each of the non-overlapping node sets in the bipartite network.
Hansen Lars Kai
Lehmann Sune
Schwartz Martin
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