An efficient community discovery algorithm based on synchronous dynamic model

摘要

Community detection is an important way to describe the evolution of network events. In order to study.how to differentiate network community structure accurately and effectively, and solve the problem of the Sync clustering algorithm directly connected nodes without common neighbor nodes existing similarity underestimated tendency, and the synchronization time being too long, we put forward a method which breaks edge disconnection between connected nodes directly and add auxiliary nodes. We can think that the two nodes are connected by the secondary node and the edge of the two nodes connected respectively, and then realize the improvement of node similarity. Secondly this paper uses neighborhood radius to divide community, and detect the community before the node object has reacded fully synchronous. Simulation experiments are done respectively on the artificial data and real data sets generated, and the results show that the improved algorithm is more accurate to effectively find community structure compared with the original algorithm.