An Online Dynamic Social Network Evolution Community Discovery Method

Abstract

Abstract Online social network in the study, dynamic implicit community or group structure of discovery and detection is a very key question at the heart of evolution, it is in the medium (Mesoscopic) view to observe the online social network hidden structure characteristic, predict the evolution trend, control of the situation, found that network abnormal mass incidents, etc. It is of great significance. The purpose of this paper is to provide technical advice for community discovery research by studying online dynamic social network evolution community discovery methods. This paper analyzes the characteristics of local clustering coefficient and node similarity calculation in unsigned network, and proposes the extended local clustering coefficient as the structural attribute of the edge in unsigned network. This property can better reflect the characteristics of local network density and network structure. Combining the new edge structure measure with the label propagation algorithm with linear time complexity, a label propagation algorithm combining the extended local clustering coefficient is proposed. The results showed that the accuracy of community discovery was improved by 32.6 percent in dynamic social networks.