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arXiv CS.AI
7/21/2026
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Original: A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges

Short summary

This survey reviews GNN-based link prediction, proposing a taxonomy that categorizes techniques by encoder architecture (GCN, GAE, GAT, GFormer) and by application domain (knowledge graphs, recommendation systems). It discusses strengths and limitations of each approach and identifies open challenges and future research directions. The work fills a gap in existing literature by focusing specifically on GNN architectures rather than general link prediction methods.

  • •Comprehensive survey of GNN-based link prediction organized by encoder architecture and application domain
  • •Covers GCN, GAE, GAT, and GFormer methods with strengths and limitations
  • •Highlights knowledge graphs and recommendation systems as key real-world applications

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