ProtGNN: Towards Self-Explaining Graph Neural Networks


  • Zaixi Zhang University of Science and Technology of China
  • Qi Liu University of Science and Technology of China
  • Hao Wang University of Science and Technology of China
  • Chengqiang Lu University of Science and Technology of China
  • Cheekong Lee Tencent America



Machine Learning (ML)


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.




How to Cite

Zhang, Z., Liu, Q., Wang, H., Lu, C., & Lee, C. (2022). ProtGNN: Towards Self-Explaining Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 36(8), 9127-9135.



AAAI Technical Track on Machine Learning III