Link prediction using deep autoencoder-like non-negative matrix factorization with L21-norm

Link prediction aims to predict missing links or eliminate spurious links and anticipate new links by analyzing observed network topological structure information. Non-negative matrix factorization(NMF) is widely used in solving the issue of link prediction due to its good interpretability and scala...

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Veröffentlicht in:Applied intelligence (Dordrecht, Netherlands) Jg. 54; H. 5; S. 4095 - 4120
Hauptverfasser: Li, Tongfeng, Zhang, Ruisheng, Yao, Yabing, Liu, Yunwu, Ma, Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.03.2024
Springer Nature B.V
Schlagworte:
Accuracy
Algorithms
Artificial Intelligence
Computer Science
Factorization
Links
Machines
Manufacturing
Mechanical Engineering
Methods
Prediction models
Processes
Random noise
Link prediction
NMF
Deep autoencoder-like NMF
norm
ISSN:0924-669X, 1573-7497
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Zusammenfassung:Link prediction aims to predict missing links or eliminate spurious links and anticipate new links by analyzing observed network topological structure information. Non-negative matrix factorization(NMF) is widely used in solving the issue of link prediction due to its good interpretability and scalability. However, most existing NMF-based approaches involve shallow decoder models, which are incapable of capturing complex hierarchical information hidden in networks, and seldom consider random noise. To address these issues, a novel deep autoencoder-like nonnegative matrix factorization method with L 2 , 1 norm for link prediction models(DANMFL) is proposed. Unlike conventional NMF-based approaches, our model contains a decoder component and an encoder component, which capture complex hierarchical information effectively, leading to more accurate prediction results. In addition, we use the L 2 , 1 norm to remove random noise in each layer and the convergence of our model is strictly proven. We conduct extensive experiments on twelve real-world networks and the experimental results show that DANMFL is superior to existing state-of-the-art baseline approaches in terms of prediction accuracy. Codes are available at https://github.com/litongf/DANMFL .
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-024-05365-6