发表机构
China University of Mining and Technology; University of Illinois at Chicago（中国矿业大学; 伊利诺伊大学芝加哥分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文从GNN视角全面综述基于GNN的链接预测，提出创新分类法，按技术和应用分类，探讨关键GNN编码器架构优缺点，突出知识图谱和推荐系统中应用，研究挑战并探讨未来方向。
AI 中文摘要
图神经网络（GNN）已成为链接预测的主导范式，能够推断缺失连接并预测潜在未来链接。然而，现有综述缺乏对基础GNN架构和多样图结构的系统探索。为填补这一关键空白，本文从新颖且专门的GNN视角对基于GNN的链接预测进行全面综述。我们提出创新分类法，基于技术和应用对近期进展进行分类。从技术角度，聚焦关键GNN编码器架构，讨论其优缺点。从应用角度，突出知识图谱和推荐系统中链接预测的突出用例，展示其现实影响。此外，研究当前挑战并探讨未来有前景的方向。
英文摘要
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.
CommentsSubmmit 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: https://doi.org/10.1002/widm.70093. Paper list at Github: https://github.com/sunxiaobei/awesome-gnn-based-link-prediction
