Supplementary: Reinforcement Learning Enhanced Explainer for Graph Neural Networks Caihua Shan

(line 4). We show our RG-Explainer for graph classification in Alg. 2. The algorithm is similar to the one explaining node classifications, except that we train our seed locator to detect the most influential (line 4). Input: The input graph G = ( V, E), node features X, node instances I, and a trained GNN model f () . Check the stopping criteria by Eq. 10. I, and a trained GNN model f () .