2 papers
cs.LG2026
Faithful, Sufficient and Understandable: Rethinking Graph Counterfactual Explanations via Discrete Diffusion Inversion
David Bechtoldt, Sidney Bender
Graph Neural Networks (GNNs) achieve strong predictive performance on graph-structured data across domains such as chemistry, biology, and network analysis, yet they provide no int…
cs.LG2025
Graph Diffusion Counterfactual Explanation
David Bechtoldt, Sidney Bender
Machine learning models that operate on graph-structured data, such as molecular graphs or social networks, often make accurate predictions but offer little insight into why certai…