  Graph Neural Networks (GNNs), which generalize the deep neural networks to
graph-structured data, have achieved great success in modeling graphs. However,
as an extension of deep learning for graphs, GNNs lack explainability, which
largely limits their adoption in scenarios that demand the transparency of
models. Though many efforts are taken to improve the explainability of deep
learning, they mainly focus on i.i.d data, which cannot be directly applied to
explain the predictions of GNNs because GNNs utilize both node features and
graph topology to make predictions. There are only very few work on the
explainability of GNNs and they focus on post-hoc explanations. Since post-hoc
explanations are not directly obtained from the GNNs, they can be biased and
misrepresent the true explanations. Therefore, in this paper, we study a novel
problem of self-explainable GNNs which can simultaneously give predictions and
explanations. We propose a new framework which can find $K$-nearest labeled
nodes for each unlabeled node to give explainable node classification, where
nearest labeled nodes are found by interpretable similarity module in terms of
both node similarity and local structure similarity. Extensive experiments on
real-world and synthetic datasets demonstrate the effectiveness of the proposed
framework for explainable node classification.
