Label Propagation Community Detection Algorithm Based on Density Peak Optimization

Abstract

Community structure detection in a complex network structure and function is used to understand network relations and find its evolution rule; monitoring and forecasting its evolution behavior have an important theoretical significance; in the epidemic monitoring, network public opinion analysis, recommendation, advertising push and combat terrorism, and safeguard national security, it has wide application prospect. A label propagation algorithm is one of the popular algorithms for community detection in recent years; the community detection algorithm based on tags that spread the biggest advantage is the simple algorithm logic, relative to the module of optimization algorithm, convergence speed is very fast, the clustering process without any optimization function, and the initialization before do not need to specify the number of complex network community. However, the algorithm has some problems such as unstable partitioning results and strong randomness. To solve this problem, this paper proposes an unsupervised label propagation community detection algorithm based on density peak. The proposed algorithm first introduces the density peak to find the clustering center, first determines the prototype of the community, and then fixes the number of communities and the clustering center of the complex network, and then uses the label propagation algorithm to detect the community, which improves the accuracy and robustness of community discovery, reduces the number of iterations, and accelerates the formation of the community. Finally, experiments on synthetic network and real network data sets are carried out with the proposed algorithm, and the results show that the proposed method has better performance.