  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.
