Research On Community Division Method Based On Node Importance

In recent years,complex networks have flourished and have become a model that can well describe social sciences,natural sciences,mathematical sciences,life sciences,and engineering technologies.By subdividing the complex network and discovering the characteristics of the structure in the network community,it can help us discover some hidden rules and laws in complex networks,and have strong application value for solving some problems in real life.Currently,researchers have proposed many methods for complex network partitioning,which provide new research ideas and directions for researchers involved in various fields of complex networks.In the research content of complex networks,the analysis of node importance is a very important factor.In order to get a better network community structure division result,there is a way to divide the network community,in this paper,a node importance evaluation function is defined in the non-directional network.Combining with the idea of spectral grading method,a community partitioning algorithm based on node importance is proposed.The empirical results show that the proposed algorithm can well divide the community structure in complex networks.Based on the node importance evaluation function,an improved algorithm is further given.After the division of the constructed smallnetwork community structure and the division of the actual network community structure,it is proved that the improved algorithm is effective and has a good division result.The algorithm and improved algorithm of this paper have important reference significance for the study of community partitioning in complex networks.