A Community Discovery Algorithm Fused with Adjacent Edge Attribute for Personal Social Network

The traditional intelligent evolution community discovery algorithms are usually have the problems such as weakening node attributes and prone to premature convergence.To address the problems,this paper proposes a community discovery algorithm,NLA/SCD,using swarm-intelligence-based clustering of adjacent edge attributes for personal social networks.By fusing the structures of adjacent edges and the similar features of their node attributes,the adaptive function of the Social Spider Optimization(SSO) algorithm is defined,and the increment of the community modularity is selected as the iterative criterion of the operator.Then,as the male and female individuals evolve and mate,the adaptive function and the modularity increment function are used to locally and globally optimize the process of the community division optimization.Experimental results show that the NLA/SCD algorithm can effectively detect the personal social networks with diverse attribute information,and it maintains a high division accuracy while running fast.