Network representation learning based on community-aware and adaptive random walk for overlapping community detection |
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Authors: | Guo Kun Wang Qinze Lin Jiaqi Wu Ling Guo Wenzhong Chao Kuo-Ming |
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Affiliation: | 1.Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, China ;2.Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou, 350116, China ;3.Institute for Advanced Manufacturing & Engineering, Coventry University, Coventry, UK ; |
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Abstract: | The Network representation learning methods based on random walk aim to learn a low-dimensional embedding vector for each node in a network by randomly traversing the network to capture the features of nodes and edges, which is beneficial to many downstream machine learning tasks such as community detection. Most of the existing random-walk-based network representation learning algorithms emphasize the neighborhood of nodes but ignore the communities they may form and apply the same random walk strategy to all nodes without distinguishing the characteristics of different nodes. In addition, it is time-consuming to determine the most suitable random walk parameters for a given network. In this paper, we propose a novel overlapping community detection algorithm based on network representation learning which integrates community information into embedding vectors to improve the cohesion degree of similar nodes in the embedding space. First, a node-centrality-based walk strategy is designed to determine the parameters of random walk automatically to avoid the time-consuming manual selection. Second, two community-aware random walk strategies for high and low degree nodes are developed to capture the characteristics of the community centers and boundaries. The experimental results on the synthesized and real-world datasets demonstrate the effectiveness and efficiency of our algorithm on overlapping community detection compared with the state-of-the-art algorithms |
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