Author(s)

Zhenping Li¹, Shihua Zhang², Xiangsun Zhang²

¹School of Information, Beijing Wuzi University, Beijing, China.
²National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Beijing, China.

In the past ten years, community detection in complex networks has attracted more and more attention of researchers. Communities often correspond to functional subunits in the complex systems. In complex network, a node community can be defined as a subgraph induced by a set of nodes, while a link community is a subgraph induced by a set of links. Although most researches pay more attention to identifying node communities in both unipartite and bipartite networks, some researchers have investigated the link community detection problem in unipartite networks. But current research pays little attention to the link community detection problem in bipartite networks. In this paper, we investigate the link community detection problem in bipartite networks, and formulate it into an integer programming model. We proposed a genetic algorithm for partition the bipartite network into overlapping link communities. Simulations are done on both artificial networks and real-world networks. The results show that the bipartite network can be efficiently partitioned into overlapping link communities by the genetic algorithm.

Bipartite Network, Link Community, Quantity Function, Integer Programming, Genetic Algorithm

Li, Z. , Zhang, S. and Zhang, X. (2015) Mathematical Model and Algorithm for Link Community Detection in Bipartite Networks. American Journal of Operations Research, 5, 421-434. doi: 10.4236/ajor.2015.55035.