改进传统图模型的链接预测方法，使其适用于有向关系数据。
Understanding the Design Principles of Link Prediction in Directed Settings
- 将无向图中的经典启发式方法适配到有向图场景。
- 新方法在多个基准上超越主流GNN模型表现。
- 为有向图链接预测提供可解释的设计原则。
链接预测是图表示学习（GRL）中建模关系数据的重要任务。早期理论基于对称邻接矩阵假设，对应无向图设置，导致大量先进研究仍沿用该对称性假设，尽管真实世界数据中关系方向蕴含关键信息。这种忽略限制了模型对有向交互复杂性的捕捉能力。本文聚焦有向链接预测，评估在无向场景下表现良好的经典启发式方法，并提出简单有效的适配策略。实验表明，这些改进方法在性能上可与专为无向图设计的领先图神经网络（GNNs）比肩。通过大规模实验，我们提炼出指导有向链接预测的新框架设计原则，该框架不仅优于基线方法，还在多个基准上超越现有最先进GNN模型。
原文摘要 · Abstract (English)
Link prediction is a widely studied task in Graph Representation Learning (GRL) for modeling relational data. The early theories in GRL were based on the assumption of a symmetric adjacency matrix, reflecting an undirected setting. As a result, much of the following state-of-the-art research has continued to operate under this symmetry assumption, even though real-world data often involve crucial information conveyed through the direction of relationships. This oversight limits the ability of these models to fully capture the complexity of directed interactions. In this paper, we focus on the challenge of directed link prediction by evaluating key heuristics that have been successful in undirected settings. We propose simple but effective adaptations of these heuristics to the directed link prediction task and demonstrate that these modifications produce competitive performance compared to the leading Graph Neural Networks (GNNs) originally designed for undirected graphs. Through an extensive set of experiments, we derive insights that inform the development of a novel framework for directed link prediction, which not only surpasses baseline methods but also outperforms state-of-the-art GNNs on multiple benchmarks.
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