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Community Detection in Complex Networks Using Nonnegative Matrix Factorization and Density-Based Clustering Algorithm

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Abstract

Community detection is a critical issue in the field of complex networks. Capable of extracting inherent patterns and structures in high dimensional data, the non-negative matrix factorization (NMF) method has become one of the hottest research topics in community detection recently. However, this method has a significant drawback; most community detection methods using NMF require the number of communities to be preassigned or determined by searching for the best community structure among all candidates. To address the problem, in this paper, we use an improved density peak clustering to obtain the number of cores as the pre-defined parameter of nonnegative matrix factorization. Then we adopt nonnegative double singular value decomposition initialization which can rapidly reduce the approximation error of nonnegative matrix factorization. Finally, we compare and analyze the performance of different algorithms on artificial networks and real-world networks. Experimental results indicate that the proposed method is superior to the state-of-the-art methods.

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Acknowledgements

This work is supported in part by Jiangsu Natural Science Foundation (No. BK20131351), by the National Natural Science Foundation of China (NSFC) (No. 61233011), by the 111 Project (No. B13022).

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Authors and Affiliations

  1. School of Computer Science, Nanjing University of Science and Technology, Xiaolingwei 200#, Nanjing, 210094, China

    Hong Lu, Xiaoshuang Sang & Jianfeng Lu

  2. College of Information Engineering, Nanjing University of Finance and Economics, Nanjing, 210023, China

    Qinghua Zhao

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  1. Hong Lu
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  2. Qinghua Zhao
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  3. Xiaoshuang Sang
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  4. Jianfeng Lu
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Correspondence to Jianfeng Lu.

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Lu, H., Zhao, Q., Sang, X. et al. Community Detection in Complex Networks Using Nonnegative Matrix Factorization and Density-Based Clustering Algorithm. Neural Process Lett 51, 1731–1748 (2020). https://doi.org/10.1007/s11063-019-10170-1

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