Identifying Modules in Complex Networks by a Graph-Theoretical Method and Its Application in Protein Interaction Networks

  • Rui-Sheng Wang
  • Shihua Zhang
  • Xiang-Sun Zhang
  • Luonan Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4682)


Detecting community structure/modules in complex networks recently attracts increasing attention from various fields including mathematics, physics and biology. In this paper, we propose a method based on graph-theoretical clustering for identifying modularity structure in complex networks. Compared with the existing algorithms, this method, based on minimum spanning tree, has several advantages. For example, unlike many algorithms, this method is deterministic and not sensitive to the initialization. In addition, the method does not require a prior knowledge about the number of the modules. It can easily obtain the number of clusters by analyzing the edge weight distribution of minimum spanning tree. Moreover, this algorithm has computational compexity of polynomial-time with low order and can be used to deal with large-scale networks. Experimental results show that our method produces good results for real networks and can also uncover meaningful functional modules in protein interaction networks.


Minimum spanning tree (MST) functional module protein interaction network complex network 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Rui-Sheng Wang
    • 1
  • Shihua Zhang
    • 3
    • 4
  • Xiang-Sun Zhang
    • 3
  • Luonan Chen
    • 2
    • 5
  1. 1.School of Information, Renmin University of China, Beijing 100872China
  2. 2.Osaka Sangyo University, Osaka 574-8530Japan
  3. 3.Academy of Mathematics and Systems Science, CAS, Beijing 100080China
  4. 4.Graduate University of Chinese Academy of Sciences, Beijing 100049China
  5. 5.Institute of Systems Biology, Shanghai University, Shanghai 200444China

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