A Stochastic Model for Detecting Heterogeneous Link Communities in Complex Networks

Discovery of communities in networks is a fundamental data analysis problem. Most of the existing approaches have focused on discovering communities of nodes, while recent studies have shown great advantages and utilities of the knowledge of communities of links. Stochastic models provides a promising class of techniques for the identification of modular structures, but most stochastic models mainly focus on the detection of node communities rather than link communities. We propose a stochastic model, which not only describes the structure of link communities, but also considers the heterogeneous distribution of community sizes, a property which is often ignored by other models. We then learn the model parameters using a method of maximum likelihood based on an expectation-maximization algorithm. To deal with large complex real networks, we extend the method by a strategy of iterative bipartition. The extended method is not only efficient, but is also able to determine the number of c...

• Type: Journal
• Year: 2015
• Where: AAAI
• Authors: Dongxiao He, Dayou Liu, Di Jin, Weixiong Zhang
• Keywords: AAAI 2015, Intelligent Agents