2008/04/23 by Nan Du, Du, Nan, Bin Wu +5
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #Data Analysis #FOS: Physical sciences #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Spam and Phishing Detection #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.0804.3636
openalex publication_date 2008/04/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recent researches have discovered that rich interactions among entities in nature and society bring about complex networks with community structures. Although the investigation of the community structures has promoted the development of many successful algorithms, most of them only find separated communities, while for the vast majority of real-world networks, communities actually overlap to some extent. Moreover, the vertices of networks can often belong to different domains as well. Therefore, in this paper, we propose a novel algorithm BiTector Bi-community De-tector) to efficiently mine overlapping communities in large-scale sparse bipartite networks. It only depends on the network topology, and does not require any priori knowledge about the number or the original partition of the network. We apply the algorithm to real-world data from different domains, showing that BiTector can successfully identifies the overlapping community structures of the bipartite networks.