Graph Neural Networks (GNNs) achieve significant performance for various learning tasks on geometric data due to the incorporation of graph structure into the learning of node representations, which renders their comprehension challenging. In this paper, we first propose a unified framework satisfied by most existing GNN explainers. Then, we introduce GraphSVX, a post hoc local model-agnostic explanation method specifically designed for GNNs. GraphSVX is a decomposition technique that captures the "fair" contribution of each feature and node towards the explained prediction by constructing a surrogate model on a perturbed dataset. It extends to graphs and ultimately provides as explanation the Shapley Values from game theory. Experiments on real-world and synthetic datasets demonstrate that GraphSVX achieves state-of-the-art performance compared to baseline models while presenting core theoretical and human-centric properties.
翻译:由于将图形结构纳入节点表示的学习过程,因此在几何数据方面的各种学习任务取得了显著的成绩,这使得它们难以理解。在本文件中,我们首先提出了一个由大多数现有GNN解释者满意的统一框架。然后,我们引入了GreaphSVX,这是专门为GNS设计的后特设本地模型-不可知性解释方法。GreagSVX是一种分解技术,它通过在环绕数据集中构建一个替代模型,为解释的预测“公平”贡献了每个特性和节点。它延伸到图表,并最终解释了游戏理论中的Shapley值。关于现实世界和合成数据集的实验表明,GregSVX在呈现核心理论和以人为本特性的同时,取得了与基线模型相比的最新性能。