  Despite the recent progress in Graph Neural Networks (GNNs), it remains
challenging to explain the predictions made by GNNs. Existing explanation
methods mainly focus on post-hoc explanations where another explanatory model
is employed to provide explanations for a trained GNN. The fact that post-hoc
methods fail to reveal the original reasoning process of GNNs raises the need
of building GNNs with built-in interpretability. In this work, we propose
Prototype Graph Neural Network (ProtGNN), which combines prototype learning
with GNNs and provides a new perspective on the explanations of GNNs. In
ProtGNN, the explanations are naturally derived from the case-based reasoning
process and are actually used during classification. The prediction of ProtGNN
is obtained by comparing the inputs to a few learned prototypes in the latent
space. Furthermore, for better interpretability and higher efficiency, a novel
conditional subgraph sampling module is incorporated to indicate which part of
the input graph is most similar to each prototype in ProtGNN+. Finally, we
evaluate our method on a wide range of datasets and perform concrete case
studies. Extensive results show that ProtGNN and ProtGNN+ can provide inherent
interpretability while achieving accuracy on par with the non-interpretable
counterparts.
