Overlapping Community Detection Method Based on Network Representation Learning and Density Peaks

Abstract

At present, the research on complex social networks has attracted extensive attention from scholars, and community detection is an important research direction in the study of network structure. Network data is often high-dimensional and very large, which makes it very difficult to process. Therefore, it is of great significance for community detection to represent network structure with low-dimensional vector. And many real world social networks contain overlapping communities. In this paper, we propose an overlapping community detection method based on network representation learning and density peaks, called NRLDP. First, it uses network representation learning technology to represent the unweighted network or weighted network with low-dimensional vectors. Then, it applies the density peaks clustering algorithm to overlapping community detection, uses cosine similarity to calculate the distance between nodes, and improves the local density calculation method. Finally, it selects the core node according to the relative distance and local density, and allocates the remaining nodes to achieve overlapping community detection of unweighted network or weighted network. Compared with relevant community detection methods on real world social networks and synthetic networks of LFR Benchmark, the results of the experiment show that our proposed approach is effective and accurate.