DAG MATTERS! GFLOWNETS ENHANCED EX-PLAINER FOR GRAPH NEURAL NETWORKS

Abstract

Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over the years. Existing literature mainly focus on selecting a subgraph, through combinatorial optimization, to provide faithful explanations. However, the exponential size of candidate subgraphs limits the applicability of state-of-the-art methods to large-scale GNNs. We enhance on this through a different approach: by proposing a generative structure -GFlowNetsbased GNN Explainer (GFlowExplainer), we turn the optimization problem into a step-by-step generative problem. Our GFlowExplainer aims to learn a policy that generates a distribution of subgraphs for which the probability of a subgraph is proportional to its' reward. The proposed approach eliminates the influence of node sequence and thus does not need any pre-training strategies. We also propose a new cut vertex matrix to efficiently explore parent states for GFlowNets structure, thus making our approach applicable in a large-scale setting. We conduct extensive experiments on both synthetic and real datasets, and both qualitative and quantitative results show the superiority of our GFlowExplainer.

1. INTRODUCTION

Graph Neural Networks (GNNs) have received widespread attention due to the springing up of graph-structured data in real-world applications, such as social networks and chemical molecules Zhang et al. (2020) . Various graph related task are widely studied including node classification Henaff et al. (2015) ; Liu et al. (2020) and graph classification Zhang et al. (2018) . However, uncovering rationales behind predictions of graph neural networks (GNNs) is relatively less explored. Recently, some explanation approaches for GNNs have gradually stepped into the public eye. There are two major branches of them: instance-level explanations and model-level explanations Yuan et al. (2022) . In this paper, we mainly focus on instance-level explanations. Instance-level approaches explain models by identifying the most critical input features for their predictions. They have four sub-branches: Gradients/Features-based Zhou et al. ( 2016 (2021) apply reinforcement learning (RL) to model-level and instance-level explanations. However, the pioneering works have some drawbacks. Perturbation-based approaches return the discrete edges for explanations, which are not as intuitive as graph generation-based approach, which could provide connected graphs. However, the task of searching connected subgraphs is a combinatorial problem, and the potential candidates increase exponentially, making most current approaches inefficient and



); Baldassarre & Azizpour (2019); Pope et al. (2019), Perturbation-based Ying et al. (2019); Luo et al. (2020); Schlichtkrull et al. (2020); Wang et al. (2020), Decompose-based Baldassarre & Azizpour (2019); Schnake et al. (2020); Feng et al. (2021) and Surrogate-based Vu & Thai (2020); Huang et al. (2022); Yuan et al. (2022). Some works such as XGNN Yuan et al. (2020) and RGExplainer Shan et al.

funding

and Huawei Noah's Ark Lab. This work was completed while Wenqian Li was a member of the Huawei Noah's Ark Lab for advanced study.

