Community Detection Algorithm Based on Density Peak Clustering and Label Propagation

The goal of community detection is to discover the structure, behavior and organization of complex networks. Label propagation algorithm is a fast and effective community detection algorithm. However, in the classic label propagation algorithm, the structural and feature information of the node is not fully utilized, and the label pro-pagation process is unstable. To address the above problems, a community detection algorithm DPC-LPA based on improved density peak clustering algorithm and label propagation algorithm in directed weighted complex network is proposed. The algorithm firstly weights the nodes according to their structure and features, which makes full use of the structural and feature information. Then it uses an improved density peak clustering algorithm to find the community center of the network and constructs the initial community accordingly, which improves the quality of community division. And then, based on node similarity and node weights, the update order of label propagation is reasonably determined, and the strength of label propagation between nodes is measured to complete label propagation, which solves the problem of unstable label propagation algorithm. Finally, on CiteSeer, Cora, WebKB, and SCHOLAT real-world datasets, the DPC-LPA algorithm is compared with DCN, WCF-LPA, and CLPE algorithms. The experimental results prove the feasibility and effectiveness of the DPC-LPA algorithm: in terms of modu-larity, the communities divided by the DPC-LPA algorithm have a more significant community structure; in terms of Adjusted Rand Index, the community division quality of the DPC-LPA algorithm is more stable; in terms of running time, the DPC-LPA algorithm has higher efficiency.