Community structure detection based on the neighbor node degree information

Li-Ying Tang, Sheng-Nan Li, Jian-Hong Lin, Qiang Guo () and Jian-Guo Liu
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Li-Ying Tang: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
Sheng-Nan Li: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
Jian-Hong Lin: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
Qiang Guo: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
Jian-Guo Liu: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China†Data Science and Cloud Service Research Centre, Shanghai University of Finance and Economics, Shanghai 200433, P. R. China

International Journal of Modern Physics C (IJMPC), 2016, vol. 27, issue 04, 1-11

Abstract: Community structure detection is of great significance for better understanding the network topology property. By taking into account the neighbor degree information of the topological network as the link weight, we present an improved Nonnegative Matrix Factorization (NMF) method for detecting community structure. The results for empirical networks show that the largest improved ratio of the Normalized Mutual Information value could reach 63.21%. Meanwhile, for synthetic networks, the highest Normalized Mutual Information value could closely reach 1, which suggests that the improved method with the optimal λ can detect the community structure more accurately. This work is helpful for understanding the interplay between the link weight and the community structure detection.

Keywords: Community structure; topological information; complex networks (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (6)

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DOI: 10.1142/S0129183116500467

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