Reviews: GNNExplainer: Generating Explanations for Graph Neural Networks

The paper focuses on graph neural networks (GNN) that has recently gained significant attention as vital components for machine learning systems over graph structured data. Specifically, the authors build a method to analyze the predictions made by GNN and output explanations in the form of contextual subgraph and subset of features of nodes in this subgraph. The goal is to understand what parts of the graph (structure and features) were given importance by GNN model while computing predictions for node and edge. The authors achieve this by optimizing an information theoretic objective that considers the mutual information between the predictions and the relevant graph components. The authors provide methods for both single and multi-instance setting.