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Open AccessArticle

Co-Association Matrix-Based Multi-Layer Fusion for Community Detection in Attributed Networks

1
Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
2
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China
3
Research Center of Big Data and Network Security, Tongji University, Shanghai 200092, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(1), 95; https://doi.org/10.3390/e21010095
Received: 3 December 2018 / Revised: 17 January 2019 / Accepted: 17 January 2019 / Published: 20 January 2019
(This article belongs to the Section Complexity)
Community detection is a challenging task in attributed networks, due to the data inconsistency between network topological structure and node attributes. The problem of how to effectively and robustly fuse multi-source heterogeneous data plays an important role in community detection algorithms. Although some algorithms taking both topological structure and node attributes into account have been proposed in recent years, the fusion strategy is simple and usually adopts the linear combination method. As a consequence of this, the detected community structure is vulnerable to small variations of the input data. In order to overcome this challenge, we develop a novel two-layer representation to capture the latent knowledge from both topological structure and node attributes in attributed networks. Then, we propose a weighted co-association matrix-based fusion algorithm (WCMFA) to detect the inherent community structure in attributed networks by using multi-layer fusion strategies. It extends the community detection method from a single-view to a multi-view style, which is consistent with the thinking model of human beings. Experiments show that our method is superior to the state-of-the-art community detection algorithms for attributed networks. View Full-Text
Keywords: community detection; attributed graph; complex networks; information fusion; data inconsistency community detection; attributed graph; complex networks; information fusion; data inconsistency
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Luo, S.; Zhang, Z.; Zhang, Y.; Ma, S. Co-Association Matrix-Based Multi-Layer Fusion for Community Detection in Attributed Networks. Entropy 2019, 21, 95.

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