关键词: 社交网络, 重叠社区, 标签传播, LeaderRank, 贡献度

Abstract: There are lots of overlapping communities in the real social networks, better detection of overlapping communities in social networks is conducive to studying network characteristics and reflecting the real situation of the networks. In order to solve the problem of the randomness of COPRA in the overlapping community of multi-label propagation, this paper proposes a label propagation algorithm based on the importance of nodes. The algorithm uses LeaderRank to calculate the importance of each node in the network, and selects the nodes of high importance to expand into a group as the pretreatment of the initial label phase, uses reasonable label update order to prevent offset pretreatment phase, and then uses the contribution degree to weaken the randomness of the label selection stage. Experimental results on benchmark networks and real networks show that the algorithm improves the quality of community discovery results.

Key words: social network, overlapping communities, label propagation, LeaderRank, contribution degree