Community Detection in Large-Scale Bipartite Networks

Community detection in networks receives much attention recently. Most of the previous works are for unipartite networks composed of only one type of nodes. In real world situations, however, there are many bipartite networks composed of two types of nodes. In this paper, we propose a fast algorithm called LP&BRIM for community detection in large-scale bipartite networks. It is based on a joint strategy of two developed algorithms -- label propagation (LP), a very fast community detection algorithm, and BRIM, an algorithm for generating better community structure by recursively inducing divisions between the two types of nodes in bipartite networks. Through experiments, we demonstrate that this new algorithm successfully finds meaningful community structures in large-scale bipartite networks in reasonable time limit.

• Publication year

  2009

• Venue

  2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology

• Publication date

  2009-09-15

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1109/WI-IAT.2009.15
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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