Interpretable Prototype-based Graph Information Bottleneck

arXiv:2310.19906v2 Announce Type: replace
Abstract: The success of Graph Neural Networks (GNNs) has led to a need for understanding their decision-making process and providing explanations for their predictions, which has given rise to explainable AI (XAI) that offers transparent explanations for black-box models. Recently, the use of prototypes has successfully improved the explainability of models by learning prototypes to imply training graphs that affect the prediction. However, these approaches tend to provide prototypes with excessive information from the entire graph, …

abstract arxiv box cs.ai cs.lg decision explainability explainable ai gnns graph graph neural networks information making networks neural networks predictions process success type understanding xai