COMRECGC：通过共同追索实现的全局图反事实解释器
COMRECGC: Global Graph Counterfactual Explainer through Common Recourse
AI总结：
本文形式化GNN全局反事实解释中的共同追索问题，提出COMRECGC算法，并在四个真实图数据集上验证其优于强基线。
摘要：
图神经网络（GNN）已被广泛应用于社交网络、分子生物学或推荐系统等各个领域。与此同时，为弥补其黑箱特性，各种GNN解释方法也应运而生。GNN预测的解释可分为两类——事实性解释和反事实解释。给定一个在“接受”和“拒绝”类别上进行二分类训练的GNN，全局反事实解释在于生成一小组与所有输入“拒绝”图相关的“接受”图。将“拒绝”图转化为“接受”图的过程称为追索。共同追索解释是一小组追索，借助它每个“拒绝”图都可以转变为“接受”图。尽管局部反事实解释已得到广泛研究，但为全局反事实解释寻找共同追索的问题仍未得到探索，尤其是在GNN方面。本文将共同追索解释问题形式化，并设计了一种有效的算法COMRECGC来解决该问题。我们在四个不同的真实世界图数据集上将该算法与强基线进行基准比较，证明了COMRECGC相较竞争方法的优越性能。我们还将共同追索解释与图反事实解释进行了比较，结果表明共同追索解释具有可比性或更优，因此值得在药物发现或计算生物学等应用中加以考虑。
英文摘要：
Graph neural networks (GNNs) have been widely used in various domains such as social networks, molecular biology, or recommendation systems. Concurrently, different explanations methods of GNNs have arisen to complement its black-box nature. Explanations of the GNNs' predictions can be categorized into two types--factual and counterfactual. Given a GNN trained on binary classification into ''accept'' and ''reject'' classes, a global counterfactual explanation consists in generating a small set of ''accept'' graphs relevant to all of the input ''reject'' graphs. The transformation of a ''reject'' graph into an ''accept'' graph is called a recourse. A common recourse explanation is a small set of recourse, from which every ''reject'' graph can be turned into an ''accept'' graph. Although local counterfactual explanations have been studied extensively, the problem of finding common recourse for global counterfactual explanation remains unexplored, particularly for GNNs. In this paper, we formalize the common recourse explanation problem, and design an effective algorithm, COMRECGC, to solve it. We benchmark our algorithm against strong baselines on four different real-world graphs datasets and demonstrate the superior performance of COMRECGC against the competitors. We also compare the common recourse explanations to the graph counterfactual explanation, showing that common recourse explanations are either comparable or superior, making them worth considering for applications such as drug discovery or computational biology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
