发现图神经网络分类差异的反事实证据，提升公平性与可解释性
Finding Counterfactual Evidences for Node Classification
- 通过特征与结构相似性对比，定位分类结果不同的节点对
- 提出索引方法和搜索算法，高效识别反事实证据
- 适用于各类GNN模型，助力公平性与准确性提升
反事实学习作为基于因果推理的新范式，有望缓解图神经网络（GNN）在公平性和可解释性方面的常见问题。然而，在许多现实场景中难以开展随机对照试验，只能依赖观测（真实）数据来推断反事实。本文提出并解决基于GNN的节点分类任务中寻找反事实证据的问题。反事实证据指一对节点：尽管在特征和邻域子图结构上高度相似，却被GNN分类为不同类别。我们设计了高效的搜索算法与一种结合节点特征与结构信息的新型索引方案，可泛化至任意GNN模型。通过多种下游应用验证，反事实证据在提升GNN公平性与准确性方面具有潜力。
原文摘要 · Abstract (English)
Counterfactual learning is emerging as an important paradigm, rooted in causality, which promises to alleviate common issues of graph neural networks (GNNs), such as fairness and interpretability. However, as in many real-world application domains where conducting randomized controlled trials is impractical, one has to rely on available observational (factual) data to detect counterfactuals. In this paper, we introduce and tackle the problem of searching for counterfactual evidences for the GNN-based node classification task. A counterfactual evidence is a pair of nodes such that, regardless they exhibit great similarity both in the features and in their neighborhood subgraph structures, they are classified differently by the GNN. We develop effective and efficient search algorithms and a novel indexing solution that leverages both node features and structural information to identify counterfactual evidences, and generalizes beyond any specific GNN. Through various downstream applications, we demonstrate the potential of counterfactual evidences to enhance fairness and accuracy of GNNs.
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