We propose a multi-explanation graph attention network (MEGAN). Unlike existing graph explainability methods, our network can produce node and edge attributional explanations along multiple channels, the number of which is independent of task specifications. This proves crucial to improve the interpretability of graph regression predictions, as explanations can be split into positive and negative evidence w.r.t to a reference value. Additionally, our attention-based network is fully differentiable and explanations can actively be trained in an explanation-supervised manner. We first validate our model on a synthetic graph regression dataset with known ground-truth explanations. Our network outperforms existing baseline explainability methods for the single- as well as the multi-explanation case, achieving near-perfect explanation accuracy during explanation supervision. Finally, we demonstrate our model's capabilities on multiple real-world datasets. We find that our model produces sparse high-fidelity explanations consistent with human intuition about those tasks.

翻译：我们提出了一种多解释图注意力网络（MEGAN）。与现有的图可解释性方法不同，我们的网络能够沿多个通道生成节点与边的归因解释，其通道数量与任务规范无关。这对于提升图回归预测的可解释性至关重要，因为解释可依据参考值分解为正向与负向证据。此外，我们的注意力网络具有完全可微性，并且解释可主动通过解释监督方式进行训练。我们首先在具有已知真实解释的合成图回归数据集上验证模型。在单解释与多解释场景中，我们的网络均优于现有基线可解释性方法，在解释监督下实现了近乎完美的解释准确率。最后，我们在多个真实数据集上展示了模型的能力。实验表明，我们的模型能够生成与人类直觉一致的高保真稀疏解释。