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.
Highlights
IntroductionBarabasi et al in "Science" Published "Emergence of Scaling in Random Networks" [2]
We propose an overlapping community detection method (NRLDP) based on network representation learning and density peaks, which considers the problemof network data representation, and considers the problem of irregular community structure in the actual network
This paper proposes an overlapping community d etection method (NRLDP) based on network representation learning and density peaks
Summary
Barabasi et al in "Science" Published "Emergence of Scaling in Random Networks" [2] The advent of these two articles represents the birth of smallworld networks and scale-free networks that are closer to the real world, opening a new era of complex network research. Researchers proposed that it is more meaningful to discover hidden laws in the network by studying social groups than studying individual users. Many complex social networks will exhibit a strong social effect The manifestation of this social effect is the formation of a variety of but closely connected groups, andthe contacts between individuals within the group are relatively frequent. If an individual is divided into multiple groups, it is an overlapping community detection, and these individuals are overlapping nodes. Overlapping community detection has important research significance
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