An Entropy Gain Ratio-based Algorithm for Community Detection in Mobile Social Networks

Abstract

Community structure is one of the important properties of complex networks, and many algorithms have been proposed to detected community based on optimization of Modularity Q. In this article, A more effective theoretic model based information gain ratio (IGA) that combines optimizing modularity and reduce the ratio of information entropy is proposed. IGA algorithms reaches better community detecting results than information entroy-based and FastGN algorithm on both computer generated by benchmark graphs and real mobile social networks. According to experimental result compared the others cluster methods in different datasets, our method tries to detecting community with low information entropy and keeping not far-off modularity.