Local Community Mining on Distributed and Dynamic Networks From a Multiagent Perspective

—Distributed and dynamic networks are ubiquitous in many real-world applications. Due to the huge-scale, decentralized, and dynamic characteristics, the global topological view is either too hard to obtain or even not available. So, most existing community detection methods working on the global view fail to handle such decentralized and dynamic large networks. In this paper, we propose a novel autonomy-oriented computing method (AOCCM) from the multiagent perspective for detecting community structures in the distributed environment. In particular, AOCCM utilizes reactive agents to pick the neighborhood node with the largest structural similarity as the candidate node, and thus determine whether it should be added into local community based on the modularity gain. We further improve AOCCM to a more efficient incremental version named AOCCM-i for mining communities from dynamic networks. AOCCM and AOCCM-i can be easily expanded to detect both non-overlapping and overlapping global co...

• Type: Journal
• Year: 2016
• Where: TCYB
• Authors: Zhan Bu, Zhiang Wu, Jie Cao, Yichuan Jiang
• Keywords: TCYB 2016