ProtGNN: Towards Self-Explaining Graph Neural Networks

Understand

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.