Survey details GNN-based link prediction techniques and applications

This paper offers a comprehensive survey of Graph Neural Network (GNN)-based link prediction techniques. It introduces a new taxonomy to categorize advancements by GNN encoder architectures, such as GCN-based, GAE-based, GAT-based, and GFormer-based methods, detailing their respective strengths and weaknesses. The survey also examines key applications, including knowledge graphs and recommendation systems, and discusses current challenges and future research directions in the field. AI

IMPACT Provides a structured overview of GNN techniques for link prediction, useful for researchers and practitioners in graph analysis and recommendation systems.

RANK_REASON The item is a survey paper on a specific AI research topic. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• GAE-based
• GAT-based
• GCN-based
• GFormer-based
• GNN-based link prediction
• graph neural networks
• knowledge graph
• Recommendation Systems

• paper
• other

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