With the ever-increasing popularity and applications of graph neural
networks, several proposals have been made to explain and understand the
decisions of a graph neural network. Explanations for graph neural networks
differ in principle from other input settings. It is important to attribute the
decision to input features and other related instances connected by the graph
structure. We find that the previous explanation generation approaches that
maximize the mutual information between the label distribution produced by the
model and the explanation to be restrictive. Specifically, existing approaches
do not enforce explanations to be valid, sparse, or robust to input
perturbations. In this paper, we lay down some of the fundamental principles
that an explanation method for graph neural networks should follow and
introduce a metric RDT-Fidelity as a measure of the explanation's
effectiveness. We propose a novel approach Zorro based on the principles from
rate-distortion theory that uses a simple combinatorial procedure to optimize
for RDT-Fidelity. Extensive experiments on real and synthetic datasets reveal
that Zorro produces sparser, stable, and more faithful explanations than
existing graph neural network explanation approaches.

本文针对图神经网络的解释方法提出了一些基本原则，并提出了一种基于率失真理论的优化方法 Zorro 来优化解释的质量，实验表明该方法相对于现有方法在稀疏性、稳定性和准确性方面均有所提高。