A Model-Centric Explainer for Graph Neural Network based Node Classification

Abstract

Graph Neural Networks (GNNs) learn node representations by aggregating a node's feature vector with its neighbors. They perform well across a variety of graph tasks. However, to enhance the reliability and trustworthiness of these models during use in critical scenarios, it is of essence to look into the decision making mechanisms of these models rather than treating them as black boxes. Our model-centric method gives insight into the kind of information learnt by GNNs about node neighborhoods during the task of node classification. We propose a neighborhood generator as an explainer that generates optimal neighborhoods to maximize a particular class prediction of the trained GNN model. We formulate neighborhood generation as a reinforcement learning problem and use a policy gradient method to train our generator using feedback from the trained GNN-based node classifier. Our method provides intelligible explanations of learning mechanisms of GNN models on synthetic as well as real-world datasets and even highlights certain shortcomings of these models.