  Explaining the foundations for predictions obtained from graph neural
networks (GNNs) is critical for credible use of GNN models for real-world
problems. Owing to the rapid growth of GNN applications, recent progress in
explaining predictions from GNNs, such as sensitivity analysis, perturbation
methods, and attribution methods, showed great opportunities and possibilities
for explaining GNN predictions. In this study, we propose a method to improve
the explanation quality of node classification tasks that can be applied in a
post hoc manner through aggregation of auxiliary explanations from important
neighboring nodes, named SEEN. Applying SEEN does not require modification of a
graph and can be used with diverse explainability techniques due to its
independent mechanism. Experiments on matching motif-participating nodes from a
given graph show great improvement in explanation accuracy of up to 12.71% and
demonstrate the correlation between the auxiliary explanations and the enhanced
explanation accuracy through leveraging their contributions. SEEN provides a
simple but effective method to enhance the explanation quality of GNN model
outputs, and this method is applicable in combination with most explainability
techniques.
