Clustering social networks using ant colony optimization

Several e-marketing applications rely on the ability to understand the structure of social networks. Social networks can be represented as graphs with customers as nodes and their interactions as edges. Most real world social networks are known to contain extremely dense subgraphs (also called as communities) which often provide critical insights about the emergent properties of the social network. The communities, in most cases, correspond to the various segments in a social system. Such an observation led researchers to propose algorithms to detect communities in networks. A modularity measure representing the quality of a network division has been proposed which on maximization yields good partitions. The modularity maximization is a strongly NP-complete problem which renders mathematical programming based optimization intractable for large problem sizes. Many heuristics based on simulated annealing, genetic algorithms and extremal optimization have been used to maximize modularity but have lead to suboptimal solutions. In this paper, we propose an ant colony optimization (ACO) based approach to detect communities. To the best of our knowledge, this is the first application of ACO to community detection. We demonstrate that ACO based approach results in a significant improvement in modularity values as compared to existing heuristics in the literature. The reasons for this improvement when tested on real and synthetic data sets are discussed.