关键词: 重叠社区, 复杂网络, [K]-核迭代因子, 节点影响力, 社区隶属度

Abstract: Overlapping community discovery algorithm based on local expansion can effectively discover overlapping communities. However, the random strategy of algorithm will cause problems such as strong randomness and poor accuracy. Therefore, a local extended class overlapping community discovery algorithm based on K-kernel iteration factor and community membership degree（KIMDOC） is proposed. KIMDOC improves K-kernel decomposition algorithm, derives K-kernel iteration factor, combines with node density, quantifies the influence of nodes, finds out the nodes with the greatest influence, improves the stability and accuracy of seed node selection. These nodes are taken as seed nodes, the membership degree of adjacent nodes are obtained. Adjacent nodes are added according to membership degree for community expansion, which improves the quality of community discovery. The algorithm is tested over benchmark networks and real-world networks. The results verify KIMDOC has high stability and accuracy.

Key words: overlapping communities, complex network, K-kernel iteration factor, node influence, community membership degree