MEGAN: MULTI-EXPLANATION GRAPH ATTENTION NETWORK

Abstract

Explainable artificial intelligence (XAI) methods are expected to improve trust during human-AI interactions, provide tools for model analysis and extend human understanding of complex problems. Explanation-supervised training allows to improve explanation quality by training self-explaining XAI models on ground truth or human-generated explanations. However, existing explanation methods have limited expressiveness and interoperability due to the fact that only single explanations in form of node and edge importance are generated. To that end we propose the novel multi-explanation graph attention network (MEGAN). Our fully differentiable, attention-based model features multiple explanation channels, which can be chosen independently of the task specifications. We first validate our model on a synthetic graph regression dataset. We show that for the special single explanation case, our model significantly outperforms existing posthoc and explanation-supervised baseline methods. Furthermore, we demonstrate significant advantages when using two explanations, both in quantitative explanation measures as well as in human interpretability. 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 and at the same time matches state-of-the-art graph neural networks in predictive performance, indicating that explanations and accuracy are not necessarily a trade-off.

1. INTRODUCTION

Explainable AI (XAI) methods aim to provide explanations complementing a model's predictions to make it's complex inner workings more transparent to humans with the intention to improve trust and reliability, provide tools for model analysis, and comply with anti-discrimination laws (Doshi-Velez & Kim, 2017) . Many explainability methods have already been proposed for graph neural networks (GNNs), as Yuan et al. (2022) demonstrate in their literature survey. However, the majority of work is focused on post-hoc XAI methods that aim to provide explanations for already existing models through external analysis procedures. In contrast to that, we demonstrate significant advantages of methods which Jiménez-Luna et al. ( 2020) call self-explaining methods. This class of models directly generates explanations alongside each prediction. One inherent advantage of many self-explaining models is their capability for explanation-supervised training. In explanation supervision the explanations are trained alongside the main prediction task to match known explanation ground truth or human-generated explanations, improving explanation quality in the process. Recently, impressive successes of explanation-supervision have been reported in the domains of image processing (Linsley et al., 2019; Qiao et al., 2018; Boyd et al., 2022) and natural language processing (Fernandes et al., 2022; Pruthi et al., 2020; Stacey et al., 2022) . In the graph domain, explanation supervision is very sparsely explored yet (Gao et al., 2021; Magister et al., 2022) . Inspired by the explanation-supervision successes demonstrated in other domains, especially by attention-based models, we propose our novel, self-explaining multi-explanation graph attention network (MEGAN) to enable effective explanation-supervised training for graph regression and classification problems. We specifically want to emphasize our focus on graph regression tasks, which have been ignored by previous work on explanation supervision. However, we argue that graph regression problems are becoming an especially important topic due to their high relevance in chemistry and material sci-1

