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

Authors:
Rui-Sheng Wang;Shihua Zhang;Xiang-Sun Zhang;Luonan Chen
Affiliations:
School of Information, Renmin University of China, Beijing 100872, China;Academy of Mathematics and Systems Science, CAS, Beijing 100080, China and Graduate University of Chinese Academy of Sciences, Beijing 100049, China;Academy of Mathematics and Systems Science, CAS, Beijing 100080, China;Osaka Sangyo University, Osaka 574-8530, Japan and Institute of Systems Biology, Shanghai University, Shanghai 200444, China
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

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Abstract

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.