Local Community Mining on Distributed and Dynamic Networks From a Multiagent Perspective
提出一种基于自主性计算的社区挖掘方法AOCCM及其增量版本AOCCM-i，利用反应式智能体根据结构相似性和模块度增益在分布式环境中逐步构建局部社区，适用于大规模、去中心化的动态网络。
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-based method for community mining (AOCCM) from the multiagent perspective 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 nonoverlapping and overlapping global community structures. Experimental results on real-life networks demonstrate that the proposed methods can reduce the computational cost by avoiding repeated structural similarity calculation and can still obtain the high-quality communities.