An Improved Louvain Algorithm for Community Detection

Abstract

Social network analysis has important research significance in sociology, business analysis, public security, and other fields. The traditional Louvain algorithm is a fast community detection algorithm with reliable results. The scale of complex networks is expanding larger all the time, and the efficiency of the Louvain algorithm will become lower. To improve the detection efficiency of large-scale networks, an improved Fast Louvain algorithm is proposed. The algorithm optimizes the iterative logic from the cyclic iteration to dynamic iteration, which speeds up the convergence speed and splits the local tree structure in the network. The split network is divided iteratively, then the tree structure is added to the partition results, and the results are optimized to reduce the computation. It has higher community aggregation, and the effect of community detection is improved. Through the experimental test of several groups of data, the Fast Louvain algorithm is superior to the traditional Louvain algorithm in partition effect and operation efficiency.