Related papers: Counterfactual Graph Learning for Link Prediction

Counterfactual Graph Learning for Link Prediction

URL: http://arxiv.org/abs/2106.02172v1
Date: Thu, 3 Jun 2021 23:27:00 GMT
Title: Counterfactual Graph Learning for Link Prediction
Authors: Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, Meng Jiang
Abstract summary: We propose a novel link prediction method that enhances graph learning by the counterfactual inference. Experiments on a number of benchmark datasets show that our proposed method achieves the state-of-the-art performance on link prediction.
Score: 34.04568972485512
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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