This work-in-progress paper proposes graph edit paths as a controlled tool for probing how GNN predictions respond to structural and attribute-level edits, and develops heuristics for generating edit paths and evaluates standard GNNs along these paths, quantifying their sensitivity to different types of edits.
DIAL, a message-passing layer that gives nodes access to graph structure through the diagonal of graph-derived operators, is introduced, which uses randomized probing to provide nodes with learnable, permutation equivariant access to diagonal entries.
Saku Peltonen, H. Bilgi, Kubilay Atasu· 0 citations
Counterfactual explanations for graph-structured data seek to determine minimal and realistic modifications required in an input graph to alter a model's prediction to a predefined output. Although counterfactual explainers that support modifying the graph by both adding and removing edges have recently emerged, there...
Maria Myrto Villia, Filippos Gouidis, T. Patkos et al.· 0 citations
Results support a conditional empirical conclusion: combining structure, topology, and stability-oriented training can improve robustness under specified structural perturbations, with dataset-dependent accuracy–stability trade-offs rather than universal guarantees.
Jelena Losic, Charles Fanning· PLOS Complex Systems· 0 citations
Experiments on synthetic and real-world datasets show that GraphK outperforms existing methods, accurately learns graph structures, and generates synthetic graphs without explicit definitions.
Resul Tugay, Eren Olug, Elif Ak et al.· 0 citations
G EO XGNN (Local Geometry Improves Explanation Robustness for Graph Neural Networks), a robust explanation framework that explicitly models the directional sensitivity of structural perturbations from a geometric perspective that significantly improves explanation robustness over existing methods.
Mengting Diao, Li Sun, Sen Su· Proceedings of the Thirty-Fi...· 0 citations
This work proposes Graph Diffusion Counterfactual Explanation via Inversion (GDCE-I), a discrete denoising diffusion model with a novel discrete inversion scheme that enables distribution-aware edits leveraging the whole domain edit space and qualitatively shows that GDCE-I attains interpretable in-distribution solutio...
David Bechtoldt, Sidney Bender· 1 citation
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