This paper presents Gem, a model-agnostic approach for providing
interpretable explanations for any GNNs on various graph learning tasks.
Specifically, we formulate the problem of providing explanations for the
decisions of GNNs as a causal learning task. Then we train a causal explanation
model equipped with a loss function based on Granger causality. Different from
existing explainers for GNNs, Gem explains GNNs on graph-structured data from a
causal perspective. It has better generalization ability as it has no
requirements on the internal structure of the GNNs or prior knowledge on the
graph learning tasks. In addition, Gem, once trained, can be used to explain
the target GNN very quickly. Our theoretical analysis shows that several recent
explainers fall into a unified framework of additive feature attribution
methods. Experimental results on synthetic and real-world datasets show that
Gem achieves a relative increase of the explanation accuracy by up to $30\%$
and speeds up the explanation process by up to $110\times$ as compared to its
state-of-the-art alternatives.

Gem 是一种通用方法，为各种图形学习任务上的任何 GNN 提供可解释性解释，通过因果学习任务解决了 GNN 决策的解释问题，并显示其相对于最先进的替代方案具有更好的解释准确性增加了高达 30％，并将解释过程加速了高达 110 倍。