A Random Iterative Algorithm for Community Detection
Research on community structure detection in complex networks has attracted a great deal of attention in recent years. In this paper we propose a random iterative algorithm to uncover meaningful communities. The algorithm starts with initial population creation. Each individual of the population is encoded with the community identifiers of the nodes in the network, so it is a potential solution of the community structure of the network considered. Nodes are randomly assigned into communities at the beginning of the algorithm. At each iteration some nodes are randomly selected, their community identifiers are reassigned according to the modularity function and the measure of information discrepancy based on the shortest path profiles of nodes in the network. In the end, a proper community structure can be detected by the identifiers encoded in the individual with the largest modularity. The algorithm does not need any prior knowledge about the number of communities and can give an appropriate number by maximizing the modularity function. The computational results of the method on real-world networks confirm its capability.
Community detection random iteration complez networks
Junhua Zhang Shihua Chen
Academy of Mathematics and Systems Science,CAS,Beijing 100190,China Key Laboratory of Random Complex College of Mathematics and Statistics,Wuhan University,Wuhan 430072,China
国际会议
The 3rd International Symposium on Optimization and System Biology(第三届最优化与系统生物学国际会议 OSB09)
张家界
英文
448-455
2009-09-20（万方平台首次上网日期，不代表论文的发表时间）