Community Detection in Graph: An Embedding Method
Junyou Zhu, Chunyu Wang, Chao Gao, Fan Zhang, Zhen Wang, Xuelong Li
Abstract
In the real world, understanding and discovering community structures of networks are significant in exploring network behaviors and functions. In addition to the effect of the closeness of edges on community detection, the node similarity and structural similarity of networks, which provide auxiliary representations of a network, are also important factors affecting the accuracy of community detection. In this paper, we first represent two similarities by measuring the degree of closeness between nodes and the similarity between two nodes far apart from each other. Then, such similarities are embedded into the low-dimensional vector space by our proposed structural equivalence embedding method based on the non-negative matrix factorization for community detection (SENMF). Extensive experiments demonstrate the effectiveness of our proposed SENMF method compared with several famous network embedding methods and traditional community detection methods.