关键词: 复杂网络, 社团结构, 聚类算法, 增量聚类, 桥系数

Abstract:

An incremental clustering algorithm is proposed to identify community structures in dynamic networks. Based on the feature that in dynamic networks there is little change in adjacent network snapshots, the community structures detected in last snapshot are used as the initial clustering results in current snapshot. Then the edge bridgeness is adopted to judge the snapshot change's influence on clustering results. Finally, the community structures fitting current snapshot are obtained by locally modifying the initial clustering results. The accuracy and efficiency of our algorithm are validated by comparing with the MCL algorithm. Experimental results demonstrate that our approach performs accurately and effectively in identifying community structures in dynamic networks because clustering the current snapshot can be avoided by incrementally analyzing the dynamic networks.

Key words: complex networks, community, clustering algorithm, incremental clustering, bridgeness