Community detection with structural and attribute similarities |
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Authors: | Fengqin Tang Wenwen Ding |
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Institution: | 1. School of Mathematics Sciences, Huaibei Normal University, Huaibei, China;2. School of Mathematics and Statistics, Lanzhou University, Lanzhou, China |
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Abstract: | An important problem in network analysis is to identify significant communities. Most of the real-world data sets exhibit a certain topological structure between nodes and the attributes describing them. In this paper, we propose a new community detection criterion considering both structural similarities and attribute similarities. The clustering method integrates the cost of clustering node attributes with the cost of clustering the structural information via the normalized modularity. We show that the joint clustering problem can be formulated as a spectral relaxation problem. The proposed algorithm is capable of learning the degree of contributions of individual node attributes. A number of numerical studies involving simulated and real data sets demonstrate the effectiveness of the proposed method. |
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Keywords: | Community detection spectral clustering modularity stochastic block model |
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