A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges
For researchers and practitioners in graph learning, this survey organizes the fragmented literature on GNN-based link prediction, but it is an incremental review without new experimental results.
This survey provides a comprehensive review of GNN-based link prediction, proposing a taxonomy based on GNN architectures and applications. It covers GCN, GAE, GAT, and GFormer methods, and discusses challenges and future directions.
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.