通过可学习的结构增强，提升新节点的链接预测能力。
Leap: Inductive Link Prediction via Learnable TopologyAugmentation
- 用可学习的拓扑增强方法融合结构与特征信息
- 在7个数据集上AUC提升最高达22%，平均精度提升17%
- 适合需要处理新增节点的动态图场景
链接预测是图机器学习众多下游应用中的关键任务。尽管图神经网络（GNN）在跨域设置中被广泛应用，但其主要适用于已知节点间的链接预测。而在实际应用中，需支持新节点的归纳式链接预测。现有方法多采用多层感知机（MLP）学习节点表示，但表达能力有限，难以捕捉图的结构信号。为此，本文提出LEAP，一种基于可学习拓扑增强的归纳式链接预测方法。不同于以往方法，LEAP同时建模结构与节点特征带来的归纳偏置，更具表达力。据我们所知，这是首个在归纳设置中通过可学习增强为新节点提供结构上下文的尝试。在7个真实世界同质与异质图上的实验表明，LEAP显著优于现有最先进方法，AUC与平均精度分别提升最高达22%和17%。代码与数据集已在GitHub公开。
原文摘要 · Abstract (English)
Link prediction is a crucial task in many downstream applications of graph machine learning. To this end, Graph Neural Network (GNN) is a widely used technique for link prediction, mainly in transductive settings, where the goal is to predict missing links between existing nodes. However, many real-life applications require an inductive setting that accommodates for new nodes, coming into an existing graph. Thus, recently inductive link prediction has attracted considerable attention, and a multi-layer perceptron (MLP) is the popular choice of most studies to learn node representations. However, these approaches have limited expressivity and do not fully capture the graph's structural signal. Therefore, in this work we propose LEAP, an inductive link prediction method based on LEArnable toPology augmentation. Unlike previous methods, LEAP models the inductive bias from both the structure and node features, and hence is more expressive. To the best of our knowledge, this is the first attempt to provide structural contexts for new nodes via learnable augmentation in inductive settings. Extensive experiments on seven real-world homogeneous and heterogeneous graphs demonstrates that LEAP significantly surpasses SOTA methods. The improvements are up to 22\% and 17\% in terms of AUC and average precision, respectively. The code and datasets are available on GitHub (https://github.com/AhmedESamy/LEAP/)
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