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Graph regularized autoencoding-inspired non-negative matrix factorization for link prediction in complex networks using clustering information and biased random walk

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Abstract

The task of link prediction has become a fundamental research problem in the analysis of complex networks. However, most existing non-negative matrix factorization (NMF) methods are decoding models and solely consider a single type of structural information around node pairs in networks, resulting in sub-optimal prediction accuracy. To solve these limitations, we propose a novel model, namely Graph Regularized Autoencoder-inspired Non-negative Matrix Factorization via jointly Clustering information and Biased random walk (GRANMFCB for short), for link prediction. Specifically, GRANMFCB comprises both encoder and decoder components, fully leveraging the advantages of autoencoders. In addition, clustering information and high-order structures are utilized to preserve abundant structural information around node pairs and against sparsity of networks. Finally, effective iterative multi-multiplication updating rules are proposed to optimize the objective function and the convergence is strictly proved. Extensive experimental results on twelve real-world networks show that our proposed model outperforms the state-of-the-art approaches. Codes are available at https://github.com/litongf/GRANMFCB.

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Acknowledgements

This work is supported by Qinghai Normal University young and middle-aged research fund project(2023QZR011), Qinghai Key Laboratory of Internet of Things, Focus on research and development and achievement transformation project in Qinghai province (Grant No: 2022-GX-155), the National Natural Science Foundation of China (No. 62366030), the Gansu Provincial Natural Science Foundation (No. 23JRRA8222), the Higher Education Innovation Fund project of Gansu (No. 2022A-022), the Open Project of Key Laboratory of Linguistic and Cultural Computing Ministry of Education (No. KFKT202304).

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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Tianshui Road, Lanzhou, 730000, China

    Tongfeng Li, Ruisheng zhang, Yunwu Liu & Jun Ma

  2. The College of Computer, Qinghai Normal University, Wusixi Road, Xining, 810016, China

    Tongfeng Li

  3. School of Computer and Commnunication, Lanzhou University of Technology, Lanzhou, 730050, China

    Yabing Yao & Jianxin Tang

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  1. Tongfeng Li
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Contributions

TL and RZ Conceptualization, Methodology, Software, Writing—original draft, Visualization, Investigation, Validation, Writing—review & editing, Supervision. YY, YL, JM and JT: Writing—review & editing, Data curation. All authors reviewed the manuscript.

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Correspondence to Tongfeng Li or Ruisheng zhang.

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Appendix A: Parameters of compared baselines

Appendix A: Parameters of compared baselines

The rest of parameters of compared baselines are set as their suggestion values. For LO, the \(\alpha\) is set to 0.0001 and the \(\beta _{1}\) of CNDP is set to 1.8. The parameters of NC and LPANMF are respectively set to 0.85 and 2.2. Besides, the layer configure information of FSSDNMF is set as [64 32 16] on the networks Convote, Filmtrust, Adolescent health, Powergrid, Physicians, Bcspr09, Football, Pdzbase, Dvdtrust, Chess and Gnutella. For network Karate, the layer configure information of FSSDNMF is set as [16 8 4].

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Li, T., zhang, R., Yao, Y. et al. Graph regularized autoencoding-inspired non-negative matrix factorization for link prediction in complex networks using clustering information and biased random walk. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06013-z

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