An ant colony optimization method to detect communities in social networks

Community detection is an important task in social network analysis. It aims to partition the network into clusters so that interactions among members within a cluster are considerably more frequent than that across clusters. A typical instantiation is to maximize the modularity of clusters which is a NP-hard problem, and thus, heuristic and meta-heuristic algorithms are employed as approximation. We present a novel divisive algorithm based on ant colony optimization to detect hierarchical community structure by maximizing the modularity. Our algorithm splits the network into two local communities iteratively and incorporates both heuristic information and pheromone trails. Experimental results on a set of synthetic benchmarks and real-world networks verified that our algorithm is highly effective for hierarchical community structure detection.