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This survey offers a critical technical deep dive into Graph Neural Network architectures for link prediction, systematically categorizing encoder methods like GCN and GAT alongside their specific challenges in knowledge graphs. By focusing on the underlying mechanics of graph structures rather than general application benefits, it provides essential insights for researchers advancing machine learning models that infer missing connections or anticipate future network states.
A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges
arXiv:2607.16198v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as the leading paradigm for link prediction, enabling the inference of missing connections and the anticipation of potential future links. However, existing reviews lack systematic exploration specifically targeting underlying GNN architectures and diverse graph structures. To address this critical gap, this paper provides a comprehensive review of GNN-based link prediction from a novel and dedicated GNN perspective. We propose an innovative taxonomy that categorizes recent advancements based on techniques and applications. From a technique perspective, we focus on key GNN encoder architectures, including GCN-based, GAE-based, GAT-based, and GFormer-based methods, discussing their strengths and limitations. From an application perspective, we highlight prominent use cases of link prediction in knowledge graphs and recommendation systems, demonstrating their real-world impact. In addition, we examine the current challenges and discuss promising future directions.
Abstract:Graph Neural Networks (GNNs) have emerged as the leading paradigm for link prediction, enabling the inference of missing connections and the anticipation of potential future links. However, existing reviews lack systematic exploration specifically targeting underlying GNN architectures and diverse graph structures. To address this critical gap, this paper provides a comprehensive review of GNN-based link prediction from a novel and dedicated GNN perspective. We propose an innovative taxonomy that categorizes recent advancements based on techniques and applications. From a technique perspective, we focus on key GNN encoder architectures, including GCN-based, GAE-based, GAT-based, and GFormer-based methods, discussing their strengths and limitations. From an application perspective, we highlight prominent use cases of link prediction in knowledge graphs and recommendation systems, demonstrating their real-world impact. In addition, we examine the current challenges and discuss promising future directions.
Submmit to WIREs: Data Mining and Knowledge Discovery. This version of the article has been accepted, after peer review but is not the version of record. The final version will be available at: this https URL. Paper list at Github: this https URL
Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
I.2.6; G.2.2; H.2.8; A.1
arXiv:2607.16198 [cs.AI]
(or arXiv:2607.16198v1 [cs.AI] for this version)
https://doi.org/10.1002/widm.70093 DOI(s) linking to related resources
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