Overlapping Community Detection In Directed Networks Based On Game Theor

In recent years,with the rapid development of the Internet and electronic information technology,many systems in our lives are abstracted into complex networks for research.The research on community structure is a key point of complex network research.Studying the community structure of complex networks can help us understand the topology and functional classification of the entire network.Therefore,community detection of complex networks is important in both the theory of networks and applications in real life.At present,the research on community detection is mainly carried out on undirected networks.However,in real life,most networks abstracted from systems are with directions,and directions of edges give useful information.Therefore,the direction of the edges of the network should not be ignored when studying directed networks.Game theory uses mathematical models to study interactions between completely rational individuals.Applying game theory to the community detection problem of networks can help us to understand the formation process of the community more clearly.In recent years,some scholars have applied game theory to the community detection problem of undirected networks,and verified that this method is effective through experiments.Based on the analysis of directed network and community detection algorithm based on game theory,this paper proposes a community detection algorithm based on game theory for directed network.Firstly,the utility function of the algorithm and the strategy of the node are proposed,because the node has to pay a certain price after joining other communities to obtain benefits,so the utility function is divided into two parts: gain function and loss function,and the node strategy includes joining,leaving,switching and unchanging.The implementation of the algorithm is to calculate the utility value of the node under different strategies,when the node cannot increase its utility value by changing the strategy,the strategy at this time is the final strategy of the node,and also indicates the community to which the node belongs,and the community structure of the network can be obtained.The proposed algorithm is experimentally verified by using real network and artificial network,and the final community structure is evaluated by modularity and standardized mutual information,and the values of modularity and standardized mutual information indicate that the proposed algorithm is effective for community detection of directed networks.Finally,by constructing the trade network between countries along the "Belt and Road",using the proposed algorithm for community detection,dividing the community structure of the trade network,and calculating the value of modularity,the results show that the algorithm proposed in this paper is effective for community detection of trade network.