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发布时间：2021-01-07

论文类型：期刊论文

发表时间：2020-11-15

发表刊物：KNOWLEDGE-BASED SYSTEMS

卷号：208

ISSN号：0950-7051

关键字：Graph embedding; Community discovery; Matrix factorization; Low rank learning; Clustering analysis

摘要：Community discovery is an important topic of network representation learning. Manifold learning has been widely applied to network representation learning. However, most manifold learning algorithms do not consider the asymmetry of edges which is not accord with the structure of social networks because the influence of nodes is not symmetrical. In this paper, a community discovery algorithm based on manifold graph embedding with structure information propagation mechanism is proposed. The proposed algorithm uses high order approximation matrix to obtain the local and global structure information of a graph, then low rank decomposition is introduced to obtain the node vectors and the context vectors. Finally, the node vectors can be adjusted by structure information. The proposed algorithm and comparison algorithms are conducted on the experimental data sets. The experimental results show that the proposed algorithm outperforms the comparison algorithms on the most experimental data sets. The experimental results prove that the proposed algorithm is an effective algorithm for community discovery. (C) 2020 Elsevier B.V. All rights reserved.