Training graph classifiers able to distinguish between healthy brains and dysfunctional ones, can help identifying substructures associated to specific cognitive phenotypes. However, the mere predictive power of the graph classifier is of limited interest to the neuroscientists, which have plenty of tools for the diagnosis of specific mental disorders. What matters is the interpretation of the model, as it can provide novel insights and new hypotheses. In this paper we propose \emph{counterfactual graphs} as a way to produce local post-hoc explanations of any black-box graph classifier. Given a graph and a black-box, a counterfactual is a graph which, while having high structural similarity with the original graph, is classified by the black-box in a different class. We propose and empirically compare several strategies for counterfactual graph search. Our experiments against a white-box classifier with known optimal counterfactual, show that our methods, although heuristic, can produce counterfactuals very close to the optimal one. Finally, we show how to use counterfactual graphs to build global explanations correctly capturing the behaviour of different black-box classifiers and providing interesting insights for the neuroscientists.

中文翻译：

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用于脑网络可解释分类的反事实图

训练能够区分健康大脑和功能失调大脑的图分类器，可以帮助识别与特定认知表型相关的子结构。然而，仅仅图分类器的预测能力对神经科学家的兴趣有限，他们有很多工具来诊断特定的精神障碍。重要的是模型的解释，因为它可以提供新的见解和新的假设。在本文中，我们提出\emph{反事实图}作为一种对任何黑盒图分类器产生局部事后解释的方法。给定一个图和一个黑盒，反事实是这样一种图，它虽然与原始图具有很高的结构相似性，但被黑盒归入了不同的类别。我们提出并根据经验比较了几种反事实图搜索策略。我们针对具有已知最佳反事实的白盒分类器的实验表明，我们的方法虽然是启发式的，但可以产生非常接近最佳反事实的反事实。最后，我们展示了如何使用反事实图来构建全局解释，正确捕捉不同黑盒分类器的行为，并为神经科学家提供有趣的见解。