Detecting Community Structure in Complex Networks by Optimal Rearrangement Clustering

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.