Detecting community structure in complex networks by optimal rearrangement clustering

Authors:
Rui-Sheng Wang;Yong Wang;Xiang-Sun Zhang;Luonan Chen
Affiliations:
School of Information, Renmin University of China, Beijing, China and Osaka Sangyo University, Osaka, Japan;State Information Center, Beijing, China and Academy of Mathematics and Systems Science, CAS, Beijing, China;Academy of Mathematics and Systems Science, CAS, Beijing, China;Osaka Sangyo University, Osaka, Japan and Institute of Systems Biology, Shanghai University, Shanghai, China
Venue:
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Year:
2007

Citing 2
Cited 1

Inferring Web communities from link topology

Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
Rearrangement Clustering: Pitfalls, Remedies, and Applications

The Journal of Machine Learning Research

Application of a unified medical data miner (UMDM) for prediction, classification, interpretation and visualization on medical datasets: the diabetes dataset case

ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects

Quantified Score

Hi-index	0.00

Visualization

Abstract

Detecting community structure in biological and social networks recently attracts increasing attention in various fields including mathematics, physics and biology. Identifying communities in complex networks can help us to understand and exploit the networks more clearly and efficiently. In this paper, we introduced a method based on a combinatorial optimization problem -- traveling salesman problem (TSP) as optimal rearrangement clustering for finding community structure in complex networks. This method can explore the global topology of a network and thus is effective in detecting modularity structure. Unlike most other algorithms for community identification, an advantage of this method is that it does not need to spend much time in finding a proper k, the number of communities in a network. We applied this method to several widely well-studied networks including a protein-protein interaction network, which demonstrates that this method is effective in detecting meaningful communities or functional modules.