Entropy-Based Graph Clustering: Application to Biological and Social Networks

Complex systems have been widely studied to characterize their structural behaviors from a topological perspective. High modularity is one of the recurrent features of real-world complex systems. Various graph clustering algorithms have been applied to identifying communities in social networks or modules in biological networks. However, their applicability to real-world systems has been limited because of the massive scale and complex connectivity of the networks. In this study, we exploit a novel information-theoretic model for graph clustering. The entropy-based clustering approach finds locally optimal clusters by growing a random seed in a manner that minimizes graph entropy. We design and analyze modifications that further improve its performance. Assigning priority in seed-selection and seed-growth is well applicable to the scale-free networks characterized by the hub-oriented structure. Computing seed-growth in parallel streams also decomposes an extremely large network efficiently. The experimental results with real biological and social networks show that the entropy-based approach has better performance than competing methods in terms of accuracy and efficiency.