Learning to predict missing links is important for many graph-based applications. Existing methods were designed to learn the observed association between two sets of variables: (1) the observed graph structure and (2) the existence of link between a pair of nodes. However, the causal relationship between these variables was ignored and we visit the possibility of learning it by simply asking a counterfactual question: "would the link exist or not if the observed graph structure became different?" To answer this question by causal inference, we consider the information of the node pair as context, global graph structural properties as treatment, and link existence as outcome. In this work, we propose a novel link prediction method that enhances graph learning by the counterfactual inference. It creates counterfactual links from the observed ones, and our method learns representations from both of them. Experiments on a number of benchmark datasets show that our proposed method achieves the state-of-the-art performance on link prediction.

本文就缺失链接预测问题提出了一种新的数据增强算法，采用因果模型来创建反事实链接，并结合观察链接和反事实链接学习图的表示，实验表明该方法在链接预测任务上具有最先进的性能。

利用反事实链接进行链接预测的学习