Study of a Community Partitioning Algorithm Based on Modularity Measurement

Abstract

The current method of community division has the characteristics of single division method and inaccurate division result. Aiming at this problem, this algorithm proposes a community partitioning algorithm based on modularity measurement, which solves the problem of accuracy of community partitioning results. Firstly, the algorithm analyses the attributes of social networks, defines the structure of social network nodes, and describes two problems of node overlap structure and node hierarchy. Secondly, iteratively calculates the influence of two structures in social networks, split the nodes that are mixed together. Thirdly, measure the similarity and community module as two evaluation indicators, and divide the social network into different communities. Finally, using the three real data sets of Karate Club Network, Dolphin Social Network and The Pol Books Dataset, this algorithm is designed to compare with other algorithms. The experimental results show that the algorithm modularity of the algorithm has higher accuracy of community division.