Bi-clique Communities

Abstract

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.

10 pages, 6 figures

References in corpus (13)

• Finding and evaluating community structure in networks
• Modularity and community structure in networks
• Uncovering the overlapping community structure of complex networks in nature and society
• The large-scale organization of metabolic networks
• Evolution of networks
• Specificity and stability in topology of protein networks
• Comparing community structure identification
• Who is the best connected scientist? A study of scientific coauthorship networks
• Quantifying social group evolution
• Modularity and community detection in bipartite networks
• Module identification in bipartite and directed networks
• The clustering coefficient and community structure of bipartite networks
• Uncovering collective listening habits and music genres in bipartite networks

Cited by in corpus (11)

• Community detection in graphs
• Complex network approaches to nonlinear time series analysis
• Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics
• Communities in Networks
• Latent geometry of bipartite networks
• Community detection in bipartite networks using weighted symmetric binary matrix factorization
• An Evolving model of online bipartite networks
• Modularity and Projection of Bipartite Networks
• Spectral coarse graining for random walk in bipartite networks
• Community detection for networks with unipartite and bipartite structure
• The art of community detection