An algorithm for detecting community structure of social networks based on prior knowledge and modularity: Research Articles

  • Authors:

  • Haifeng Du;Marcus W. Feldman;Shuzhuo Li;Xiaoyi Jin

  • Affiliations:

  • Inst. for Popl. and Dev. Studies, Sch. of Pub. Policy and Admin., Xi'an Jiaotong Univ., Xi'an, Shaanxi Prov., 710049, China and Morrison Inst. for Popl. and Res. Studies, Stanford Univ., Stanford, ...;Morrison Institute for Population and Resource Studies, Stanford University, Stanford, California 94305;Institute for Population and Development Studies, School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710049, China;Inst. for Popl. and Dev. Studies, Sch. of Pub. Policy and Admin., Xi'an Jiaotong Univ., Xi'an, Shaanxi Province, 710049, China and Morrison Inst. for Popl. and Res. Studies, Stanford Univ., Stanfo ...

  • Venue:
  • Complexity
  • Year:
  • 2007

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Abstract

An algorithm is proposed to detect community structure in social network. The algorithm begins with a community division based on prior knowledge of the degrees of the nodes, and then combines the communities until a clear partition is obtained. In applications such as a computer-generated network, Ucinet networks, and Chinese rural-urban migrants' social networks, the algorithm can achieve higher modularity and greater speed than others in the recent literature. © 2007 Wiley Periodicals, Inc. Complexity 12: 53–60, 2007