| In recent years,the research of complex networks has become a research hotspot.Many of the complex systems in reality,such as,urban road traffic networks,micro-blog user networks,can all be abstracted as complex networks.And community structure detection has become a focus of research in the field of complex networks.The community structure is composed of communities.Nodes in the same community are closely connected and nodes in different communities are sparsely connected.Studying the community structure of complex networks will help people understand the network functions more comprehensively,and more accurately predict the evolution of the network.Game theory is a theory that studies the interaction of strategies between participants.Game theory can be used to explain the community’s top-down formation process in complex networks.In recent years,many researchers have applied game theory to community discovery and modeled the process of detecting network community structure as a game for the community.The research has achieved good results and proved the validity and rationality of game theory for community discovery.Based on the analysis of existing community discovery algorithms and community detection algorithm based on game theory,this paper proposes an overlapping community discovery algorithm based on game theory.Mainly complete the following:(1)This paper proposes a utility function based on node attributes.In order to obtain more accurate results of community partitioning,the existing algorithm does not consider the issue that node attributes will affect the selection of node strategies.This paper joins the ratio of node degrees to the degrees of all nodes in the community to which they are joined to obtain a new gain function.Since the node joins the new community,it pays a corresponding price.Therefore,the node’s utility function is the difference between the node gain function and the loss function.(2)This paper proposes a community detection game algorithm based on node importance ranking.For the effect of node attributes on the order of the nodes in the network for policy selection,this paper ranks the nodes in descending order of importance,and in turn selects strategies to increase revenue.The node’s strategy in this algorithm is to join the community,leave the community,and transform the community.Finally,the proposed algorithm is compared with the existing algorithm in different real networks and artificial networks.The results show that the proposed algorithm is superior to other algorithms. |
