Multi-Layer Network Local Community Detection Based on Influence Relation

In recent years, the discovery of local communities in multi-layer networks has become an active research field of complex systems.Such as communication, social networking, sensor network the rapid development of new technologies, the amount of data generated by increased, all want to obtain the network information difficulty is big, and the network, community, mutual influence between nodes, greatly enhance the complexity of network and make local found existing multilayer network method is unable to get more accurate test results.In this paper, based on the homogeneity drive of multi-layer network and the influence relation index of multi-layer path length measurement, a local community detection model based on the influence relation of the multi-layer network is proposed by combining the direct influence relation and indirect influence relation of the network (IMLC).Compared with six real multi-layer network data sets, the algorithm has better robustness in many most advanced multi-layer methods: GL, PMM, and ML-LCD.