Research On The Structure Of Community In Social Network Based On Propagation

Community structure is the basic feature of the network.Analyzing the community structure and community evolution law in social networks will help us better understand the composition of the social str ucture,which is important to analyze the complex network topology,understand and control network information propagation and the evolution of network public opinion play an important role.In order to understand the influence of individual network users’ communication behaviors and network groups in information dissemination on the structure of communities,and to discover the relationship s between network information propagation and community evolution.A social network community detection algorithm based on propagation was proposed.Using the topological structure of social network and the defined information propagation model of social network,the communication behavior of network users in social network and the relationship affected by the group,researched the evolution process and influencing factors of community structure in social network.The content consists of four parts in this paper.The first is to propose the idea that users with the same communication behavior may belong to the same community based on the basic characteristics of the complex network community structure,such as the characteristics of nodes within the same community having the same attributes.The second is to construct a new information propagation model based on the classic virus transmission model to simulate the information dissemination process in social networks.The third is to propose novel community division algorithm based on the network model and propagation model.The last is to verify the rationality and effectiveness of the proposed algorithm through simulation experiments and the comparison experiments with real data.The specific details are as follow.(1)Community detection in social networks based on information propagation and user engagement.Information propagated in social networks.The analysis of propagation phenomenon in social networks is of great significance to the quantification of the influence of different propagation methods of individual users in social networks.Quantified the propagation influence of users by defining the user engagement,and constructed the network model.The definitions of node activity,node propagation strength and edge strength are proposed.The classic SI virus propagation model is used to simulate information propagation of social network,and proposed the community detection algorithm to divide the community structure and overlapping communities in the social networks.Experiments have shown that network propagation and community division affect each other,and community structures are significantly different in network commun ication of different scales.(2)Crowd attraction driven community evolution on social network.The influence of information propagation will develop from individual-to-individual propagation to group-to-group propagation.Based on this phenomenon,the concept of crowd attraction was proposed to simulate the group influence that individual users received when participating in information dissemination.Constructed a new network model by establishing new information propagation model SIP model and defining the concepts of basic communities,seed nodes,basic community activity and crowd attraction.Proposed community detection algorithm based on crowd attraction to find the community structure in social network.The experiments show that the algorithm can be used to divide the community structure in real networks,and the network including the final communities has higher modularity,which can be used to detect the community structure of real-world networks.