  Explaining Graph Neural Networks predictions to end users of AI applications
in easily understandable terms remains an unsolved problem. In particular, we
do not have well developed methods for automatically evaluating explanations,
in ways that are closer to how users consume those explanations. Based on
recent application trends and our own experiences in real world problems, we
propose automatic evaluation approaches for GNN Explanations.
