With the increasing use of Graph Neural Networks (GNNs) in critical
real-world applications, several post hoc explanation methods have been
proposed to understand their predictions. However, there has been no work in
generating explanations on the fly during model training and utilizing them to
improve the expressive power of the underlying GNN models. In this work, we
introduce a novel explanation-directed neural message passing framework for
GNNs, EXPASS (EXplainable message PASSing), which aggregates only embeddings
from nodes and edges identified as important by a GNN explanation method.
EXPASS can be used with any existing GNN architecture and subgraph-optimizing
explainer to learn accurate graph embeddings. We theoretically show that EXPASS
alleviates the oversmoothing problem in GNNs by slowing the layer wise loss of
Dirichlet energy and that the embedding difference between the vanilla message
passing and EXPASS framework can be upper bounded by the difference of their
respective model weights. Our empirical results show that graph embeddings
learned using EXPASS improve the predictive performance and alleviate the
oversmoothing problems of GNNs, opening up new frontiers in graph machine
learning to develop explanation-based training frameworks.

该研究提出了一种基于解释的神经信息传递框架（EXPASS），用于提高图神经网络的表达能力并解决过度平滑问题，该框架可以与任何现有图神经网络结构和子图优化模型结合使用，并在实验中展示出其提高了预测能力的效果。