系统梳理GNN在链接预测中的技术与应用，揭示方法优劣与未来方向。
A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges
- 按GNN架构分类：GCN、GAE、GAT、GFormer等编码器方法
- 在知识图谱与推荐系统中验证实际效果，支持真实场景应用
- 剖析当前挑战并提出未来研究方向，适合想深入GNN的读者
图神经网络（GNN）已成为链接预测的主流范式，能够推断缺失连接并预测潜在未来链接。然而，现有综述未能系统探讨底层GNN架构与多样化图结构之间的关系。为此，本文从专门的GNN视角出发，全面回顾基于GNN的链接预测技术。我们提出一种创新分类体系，按技术与应用双维度组织近年进展。技术层面聚焦关键的GNN编码器架构，包括基于GCN、GAE、GAT和GFormer的方法，分析其优势与局限性。应用层面重点展示链接预测在知识图谱与推荐系统中的典型应用场景，体现其现实价值。此外，还讨论当前面临的挑战，并展望有前景的未来方向。
原文摘要 · Abstract (English)
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.
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