Label Propagation Community Detection Algorithm Based on Density Peak Optimization

Abstract

Community structure detection in complex network structure and function to understand network relations, found its evolution rule, monitoring and forecasting its evolution behavior has important theoretical significance, in the epidemic monitoring, network public opinion analysis, recommendation, advertising push and combat terrorism and safeguard national security has wide application prospect. Label propagation algorithm is one of the popular algorithms for community detection in recent years, the community detection algorithm based on tags 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.

An Improved Density Peak Clustering Algorithm Based on Chebyshev Inequality and Differential Privacy

“…Set x i as the ith sample of the dataset D, ρ as the local density, and ρ i to represent the local density of each point. There are two methods to define the local density [33]: if all attribute types of the dataset D are discrete, Equation (5), which represents the calculation method of truncated kernel, can be used; if the attribute types of the dataset D are continuous, the soft statistics method, which is based on Gaussian kernel, can be used, and its formula is given by Equation ( 6) [29].…”

Label Propagation Community Detection Algorithm Based on Density Peak Optimization

“…Ma Yan and Chen Guoqiang current address is School of Computer and Information Engineering, Henan University, Henan Province, China. This work was outlined at the 2021 17th International Conference on Computational Intelligence and Security (CIS) [38].…”