2026-07-21 04:00 UTCOriginal source2 min readUpdated: 2026-07-21 08:15 UTC

A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges

This paper provides a comprehensive review of GNN-based link prediction from a novel GNN perspective, proposing a taxonomy covering GCN, GAE, GAT, and GFormer architectures, highlighting applications in knowledge graphs and recommendations, and discussing challenges and future directions.

SourcearXiv AIAuthor: Chengcheng Sun, Yajie Song, Cheng Zhai, Jiayun Tian, Jia Yang, Xiaobin Rui, Jian Zhang, Zhixiao Wang, Philip S. Yu

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[Submitted on 29 Apr 2026]

Title:A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges

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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.

Comments: 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

Subjects:

Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Social and Information Networks (cs.SI)

MSC classes: 68T07, 68R10

ACM classes: I.2.6; G.2.2; H.2.8; A.1

Cite as: arXiv:2607.16198 [cs.AI]

(or arXiv:2607.16198v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.16198

arXiv-issued DOI via DataCite

Related DOI:

https://doi.org/10.1002/widm.70093

DOI(s) linking to related resources

Submission history

From: Chengcheng Sun [view email] [v1] Wed, 29 Apr 2026 01:34:00 UTC (884 KB)

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