  Subgraph-enhanced graph neural networks (SGNN) can increase the expressive
power of the standard message-passing framework. This model family represents
each graph as a collection of subgraphs, generally extracted by random sampling
or with hand-crafted heuristics. Our key observation is that by selecting
"meaningful" subgraphs, besides improving the expressivity of a GNN, it is also
possible to obtain interpretable results. For this purpose, we introduce a
novel framework that jointly predicts the class of the graph and a set of
explanatory sparse subgraphs, which can be analyzed to understand the decision
process of the classifier. We compare the performance of our framework against
standard subgraph extraction policies, like random node/edge deletion
strategies. The subgraphs produced by our framework allow to achieve comparable
performance in terms of accuracy, with the additional benefit of providing
explanations.
