Can I read SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks on EtoBox?

SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks by Huang, Zhenhua; Li, Kunhao; Wang, Shaojie; Jia, Zhaohong; Zhu, Wentao; Mehrotra, Sharad is a scholarly article available to read on EtoBox.

What is SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks about?

Despite the Graph Neural Networks' (GNNs) proficiency in analyzing graph data, achieving high-accuracy and interpretable predictions remains challenging. Existing GNN interpreters typically provide post-hoc explanations disjointed from GNNs' predictions, resulting in misrepresentations. Self-explainable GNNs offer built-in explanations during the training process. However, they cannot exploit the explanatory outcomes to augment prediction performance, and they fail to provide high-quality explanations of node features and require additional processes to generate explainable subgraphs, which is costly. To address the aforementioned limitations, we propose a self-explained and self-supervised graph neural network (SES) to bridge the gap between explainability and prediction. SES comprises two processes: explainable training and enhanced predictive learning. During explainable training, SES employs a global mask generator co-trained with a graph encoder and directly produces crucial structure and feature masks, reducing time consumption and providing node feature and subgraph explanations. In the enhanced predictive learning phase, mask-based positive-negative pairs are constructed ut

Author

Huang, Zhenhua; Li, Kunhao; Wang, Shaojie; Jia, Zhaohong; Zhu, Wentao; Mehrotra, Sharad

Published

2024

Language

EN