Community detection in social networks using ant colony algorithm and fuzzy clustering

Nowadays, social networks with hundreds of millions user are regarded as powerful tools to conduct the information flow about communications in modern societies. During the last decade, researchers have made a huge attention on studying and analysis of different aspects of these networks. A curiosity property of these networks is the presence of communities (or clusters), which represent subsets of nodes within the network such that the number of edges between nodes in the same community is large whereas the number of edges connecting nodes in different communities is small. In this paper, we suggest a bipartite algorithm for finding communities in social networks. First, we use artificial ants to traverse the network modeled by a graph based on a set of rules to find a “good region” of edges that are likely to connect nodes within a community. Using these edges we construct the communities after which local optimization methods are used to further improve the solution quality. Next, we use a fuzzy clustering algorithm called Fuzzy C-Means (FCM) to fine tune the result achieved in the first phase. Experimental results on several synthetic and real world networks show that the algorithm is very competitive against current state-of-the-art techniques for community detection. In particular, our algorithm is more accurate than existing algorithms as it performs well across many different types of networks.