  Graph Neural Networks (GNNs) are a popular class of machine learning models.
Inspired by the learning to explain (L2X) paradigm, we propose L2XGNN, a
framework for explainable GNNs which provides faithful explanations by design.
L2XGNN learns a mechanism for selecting explanatory subgraphs (motifs) which
are exclusively used in the GNNs message-passing operations. L2XGNN is able to
select, for each input graph, a subgraph with specific properties such as being
sparse and connected. Imposing such constraints on the motifs often leads to
more interpretable and effective explanations. Experiments on several datasets
suggest that L2XGNN achieves the same classification accuracy as baseline
methods using the entire input graph while ensuring that only the provided
explanations are used to make predictions. Moreover, we show that L2XGNN is
able to identify motifs responsible for the graph's properties it is intended
to predict.
