Explaining predictions made by machine learning models is important and has attracted increased interest. The Shapley value from cooperative game theory has been proposed as a prime approach for computing feature importance towards predictions, especially for images, text, tabular data, and recently graph neural networks (GNNs) on graphs. In this work, we revisit the appropriateness of the Shapley value for graph explanation, where the task is to identify the most important subgraph and constitu…