Research On Complex Network Overlapping Community Discovery Algorithm Based On Evolutionary Algorithm

Most of the complex systems in the real world can be abstracted into complex network models,and the most studied objects in the network models are the community structure.The interaction of the communities in the network forms the whole function of the network.Therefore,the discovery of community structure is important in the research of complex network.In all the community discovery algorithms,the community discovery algorithm based on genetic algorithm is a new field with good research prospects in recent years.Genetic algorithm is a method to search the optimal solution by simulating the natural evolution process.This paper is based on the idea of genetic algorithm as a starting point design of an evolutionary algorithm FEA(Fang Evolutionary algorithm),FEA can not only find the community structure,but also find overlapping community structure.In the study of complex networks,the number of communities in most of the complex networks of the real world is not known.So many network algorithms which need to know the number of communities in advance cannot be used for network communities discovery.Therefore,in order to know the prior knowledge of the number of communities in advance,this thesis applies the network topology of the Laplace matrix Jordan standard to obtain the prior knowledge of the number of network communities.According to the number of network community,based on the modularity function Q as the evaluation function,the FEA algorithm is used to search the whole network,and finally the overlapping community structure is found.In the classical network,it is verified that the FEA algorithm proposed in this thesis conforms to the real situation of the network and agrees with the results of the previous researches,which shows the feasibility and correctness of the FEA algorithm and succeeds in applying the evolutionary algorithm to the detection of overlapping community structures.