Prototype-Based Explanations for Graph Neural Networks (Student Abstract)
Keywords:Explanable Method, Graph Neural Network, Graph-level Classification, Prototype, Clustering
AbstractAside the high performance of graph neural networks (GNNs), considerable attention has recently been paid to explanations of black-box deep learning models. Unlike most studies focusing on model explanations based on a specific graph instance, we propose Prototype-bAsed GNN-Explainer (PAGE), a novel model-level explanation method for graph-level classification that explains what the underlying model has learned by providing human-interpretable prototypes. Specifically, our method performs clustering on the embedding space of the underlying GNN model; extracts embeddings in each cluster; and discovers prototypes, which serve as model explanations, by estimating the maximum common subgraph (MCS) from the extracted embeddings. Experimental evaluation demonstrates that PAGE not only provides high-quality explanations but also outperforms the state-of-the-art model-level method in terms of consistency and faithfulness that are performance metrics for quantitative evaluations.
How to Cite
Shin, Y.-M., Kim, S.-W., Yoon, E.-B., & Shin, W.-Y. (2022). Prototype-Based Explanations for Graph Neural Networks (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13047-13048. https://doi.org/10.1609/aaai.v36i11.21660
AAAI Student Abstract and Poster Program