Graph regularized autoencoding-inspired non-negative matrix factorization for link prediction in complex networks using clustering information and biased random walk-Reference-Cited by-同舟云学术

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

Published:2024-03-20 Issue:10 Volume:80 Page:14433-14469
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Li Tongfeng,zhang Ruisheng,Yao Yabing,Liu Yunwu,Ma Jun,Tang Jianxin

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11227-024-06013-z.pdf

Reference68 articles.

1. Kumar A, Singh SS, Singh K et al (2020) Link prediction techniques, applications, and performance: a survey. Physica A: Stat Mech Appl 553:124289

2. Wang W, Feng Y, Jiao P et al (2017) Kernel framework based on non-negative matrix factorization for networks reconstruction and link prediction. Knowl-Based Syst 137(Dec 1):104–114

3. Yao Y, Zhang R, Yang F et al (2018) Link prediction in complex networks based on the interactions among paths. Physica A: Stat Mech Appl 510:52–67

4. Yao Y, Cheng T, Li X et al (2023) Link prediction based on the mutual information with high-order clustering structure of nodes in complex networks. Physica A: Stat Mech Appl 610:128428

5. Akbar JA, Anthony K, Wu FX (2020) MDIPA: a microRNA-drug interaction prediction approach based on non-negative matrix factorization. Bioinformatics 36(20):5061–5067

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