explainability

Counterfactual Explanation (CE) techniques have garnered attention as a means to provide insights to the users engaging with AI …

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 …

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 …

Open source framework for evaluating Graph Counterfactual Explanation (GCE) methods. Our main goal is to create a generic platform that allows the researchers to speed up the process of developing and testing new GCE methods.