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An augmented Lagrangian alternating direction method for overlapping community detection based on symmetric nonnegative matrix factorization

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Abstract

In this paper, we present an augmented Lagrangian alternating direction algorithm for symmetric nonnegative matrix factorization. The convergence of the algorithm is also proved in detail and strictly. Then we present a modified overlapping community detection method which is based on the presented symmetric nonnegative matrix factorization algorithm. We apply the modified community detection method to several real world networks. The obtained results show the capability of our method in detecting overlapping communities, hubs and outliers. We find that our experimental results have better quality than several competing methods for identifying communities.

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Acknowledgements

The authors are grateful to Prof. Z. Peng of Fuzhou University for his guidance and suggestions on optimization theory in this work. The authors thank the editors and the anonymous reviewers for their insightful and constructive comments. This work is supported by National Natural Science Foundation of China (Nos. U1805263, 11672074, 61772134), Natural Science Foundation of Fujian Province (Nos. 2018J01775, 2018J01776) and Fujian Science and Technology Department Project (No. JK2017007).

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  1. School of Mathematics and Information and Digital Fujian Internet-of-things Laboratory of Environmental Monitoring, Fujian Normal University, Fuzhou, China

    Liying Hu & Gongde Guo

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  1. Liying Hu
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  2. Gongde Guo
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Correspondence to Gongde Guo.

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Hu, L., Guo, G. An augmented Lagrangian alternating direction method for overlapping community detection based on symmetric nonnegative matrix factorization. Int. J. Mach. Learn. & Cyber. 11, 403–415 (2020). https://doi.org/10.1007/s13042-019-00980-z

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