Based On The Degree Of Interaction Found In Large-scale Social Networking Communities

With the explosion of the Internetin recentyears, communication between people become more frequently.As a result,the social networks also enjoy rapid development. Social networks consist of collection of individuals, organizations and their relationships (interaction). The discovery of large-scale social network gradually becomes the hottest issue in the study of social networks. Social network is closely related to daily life. For example, social political and economic activities, daily communication between friends and the cooperation of scientistscan be studied from the perspective of social network. By studying social network structure, we can analyze the propagation law of information and get together people who share common interests, in order to provide people with a better communication platform or tool and prevent the spread of bad information. Based on the interaction of people, this paper puts forward "interaction degree" which is used to measure interaction between the members of the network. Moreover, a discovery algorithm of large social network community on the basis of interaction degree is introduced in this paper. The main part of this paper is summarized as follows:(1)Put forward a hierarchical discovery algorithm of large social network community. The core problem of large-scale network is the scale.This paper proposes adiscovery algorithm of large-scale social network community in line with the hierarchical clustering. Firstly, preprocessing should be done by dividing the large-scale network into small and local one, and found small network communities in the first large-scale network. Then we should treatcommunities in the small local network as a point and reconstruct the networks in the post-processing phase. Based on the reconstruction of the networks, the discovery algorithm of large social network community can be presented.(2)Complete the measuring of interaction from local to global based on the calculation of interaction degree. After the large-scale network partitioning, through the calculation of interaction degree, the interaction degree between the communities and the members can betransmittedfrom local to global and maintain the consistency of the measurement to ensure the accuracy of the results.(3)Algorithm has been applied to real social network and artificial simulation network. This paper tests thealgorithm respectively in terms of accuracy and efficiency of it and illustrates the effectiveness and efficiency of the algorithm..In summary, the work of this paper is to face in the context of social network analysis, the proposed algorithm based on the interaction of the discovery of large-scale social network community, and be able to get a more precise division of the community.