A comprehensive survey of link prediction methods

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

T Li, R Zhang, Y Yao, Y Liu, J Ma - Applied Intelligence, 2024 - Springer
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 …
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DMGAE: An interpretable representation learning method for directed scale-free networks based on autoencoder and masking

QC Yang, K Yang, ZL Hu, M Li - Information Processing & Management, 2025 - Elsevier
Although existing graph self-supervised learning approaches have paid attention to the
directed nature of networks, they have often overlooked the ubiquitous scale-free attributes …
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Quantifying discriminability of evaluation metrics in link prediction for real networks

S Wan, Y Bi, X Jiao, T Zhou - arxiv preprint arxiv:2409.20078, 2024 - arxiv.org
Link prediction is one of the most productive branches in network science, aiming to predict
links that would have existed but have not yet been observed, or links that will appear during …
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Enhancing link prediction through node embedding and ensemble learning

Z Chen, Y Wang - Knowledge and Information Systems, 2024 - Springer
Social networks, characterized by their dynamic and continually evolving nature, present
challenges for effective link prediction (LP) due to the constant addition of nodes and …
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Dual stream fusion link prediction for sparse graph based on variational graph autoencoder and pairwise learning

X Li, H Cai, C Feng, A Zhao - Information Processing & Management, 2025 - Elsevier
Recently, link prediction methods based graph neural networks have garnered significant
attention and achieved great success on large datasets. However, existing methods usually …
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A novel recommendation-based framework for reconnecting and selecting the efficient friendship path in the heterogeneous social IoT network

B Farhadi, P Asghari, E Mahdipour, HHS Javadi - Computer Networks, 2025 - Elsevier
Automating the selection process for the most suitable service in a dynamic Internet of
Things (IoT) ecosystem to improve critical metrics such as resilience, throughput, delay …
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Group link prediction in bipartite graphs with graph neural networks

S Luo, H Li, J Huang, X Ma, J Cui, S Qiao, J Yoo - Pattern Recognition, 2025 - Elsevier
Group link prediction is of both theoretical and practical significance since it can be used to
analyze relationships between individuals and groups. However, obeying the homophily …
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Graph regularized autoencoding-inspired non-negative matrix factorization for link prediction in complex networks using clustering information and biased random …

T Li, R Zhang, Y Yao, Y Liu, J Ma, J Tang - The Journal of Supercomputing, 2024 - Springer
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 …
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Finding future associations in complex networks using multiple network features

RK Yadav, SP Tripathi, AK Rai - The Journal of Supercomputing, 2025 - Springer
Finding future or missing associations is an essential problem in complex systems because
of its numerous application areas. These applications use link prediction methods to provide …
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Multi-Class and Multi-Task Strategies for Neural Directed Link Prediction

C Moroni, C Borile, C Mattsson, M Starnini… - arxiv preprint arxiv …, 2024 - arxiv.org
Link Prediction is a foundational task in Graph Representation Learning, supporting
applications like link recommendation, knowledge graph completion and graph generation …
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