explainability

The lab provides a hands-on experience for users to develop and evaluate Graph Counterfactual Explanation methods using the GRETEL framework, covering challenges, building pipelines, customizing solutions, and analyzing performance in diverse datasets.

The tutorial covers generating counterfactual explanations for Graph Neural Networks, addressing theoretical foundations, challenges, definitions, methods, and benchmarking for improved interpretability.

Graph Neural Networks (GNNs) perform well in community detection and molecule classification. Counterfactual Explanations (CE) provide …

We introduce a novel semi-supervised Graph Counterfactual Explainer (GCE) methodology, Dynamic GRAph Counterfactual Explainer …

Counterfactual explainability (CE) has been widely explored in various domains ranging from medical image diagnosis to self-driving …

The black-box nature and the lack of interpretability detract from constant improvements in Graph Neural Networks (GNNs) performance in …

In recent years, Graph Neural Networks have reported outstanding performances in tasks like community detection, molecule …