• explainability
• graph generation
• graph neural network
• reinforcement learning

Deep Graph Networks are a set of powerful models for solving complex graph-related tasks and have become an interesting research topic that has been consistently growing in popularity in the latest years. However, providing explanations for their predictions is an extremely difficult time-demanding task and is still an open research area. In contrast to the significant amount of work that has been done for the interpretation of vectorial deep learning models, explainability on deep graph networks is still a wide unexplored area.
Based on the current literature, this work tackles the explainability problem for deep graph networks by generating counterfactual explanations in a chemical context. Given an instance and its prediction made by the deep graph network being explained, the generation is formulated as a reinforcement learning problem, in which the generative agent learns to modify the original query graph the least, in order to obtain a substantial change of prediction. Finally, we employ GNNExplainer, a model-agnostic local interpretation method, to produce explanations on both the input graph and its counterfactual explanations, in order to analyse how it behaves in the range of a given input.