GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks

Abstract

Recently, Graph Neural Networks (GNNs) have significantly advanced the performance of machine learning tasks on graphs. However, this technological breakthrough makes people wonder: how does a GNN make such decisions, and can we trust its prediction with high confidence? When it comes to some critical fields, such as biomedicine, where making wrong decisions can have severe consequences, it is crucial to interpret the inner working mechanisms of GNNs before applying them. In this paper, we propose a model-agnostic model-level explanation method for different GNNs that follow the message passing scheme, GNNInterpreter, to explain the high-level decision-making process of the GNN model. More specifically, GNNInterpreter learns a probabilistic generative graph distribution that produces the most discriminative graph pattern the GNN tries to detect when making a certain prediction by optimizing a novel objective function specifically designed for the model-level explanation for GNNs. Compared with the existing work, GNNInterpreter is more computationally efficient and more flexible in generating explanation graphs with different types of node features and edge features, without introducing another blackbox to explain the GNN and without requiring manually specified domain-specific knowledge. Additionally, the experimental studies conducted on four different datasets demonstrate that the explanation graph generated by GNNInterpreter can match the desired graph pattern when the model is ideal and reveal potential model pitfalls if there exist any