Efficient Vector Influence Clustering Coefficient Based Directed Community Detection Method

Abstract

Community detection algorithms are important for determining the character statistics of complex networks. Compared with the conventional community detection algorithms, which always focus on undirected networks, our algorithm is concentrated on directed networks such as the WeChat moments relationship network and the Sina Micro-Blog follower relationship network. To address disadvantages such as lower execution efficiency and higher deviation of precision that current directed community detection algorithms always have, we propose a new approach that is based on the triangle structure of community basis and modeled on the local information transfer process to precisely detect communities in directed networks. Based on the directed vector theory in probability graphs and the dynamic information transfer gain (ITG) of vertices in directed networks, we propose the novel ITG method and the corresponding target optimal function for evaluating the partition quality in a community detection algorithm. Then, we combine ITG and the target function to create the new community detection algorithm ITG-directed weighted community clustering for directed networks. With extensive experiments using artificial network data sets and large, real-world network data sets derived from online social media, our algorithm proved to be more accurate and faster in directed networks than several traditional, well-known community detection methods, such as FastGN, order statistics local optimization method, and Infomap.