Generating In-Distribution Counterfactual Explanation for Graph Neural Networks

Linmao Chen; Chaobo He; Junwei Cheng; Chunying Li; Quanlong Guan

AAAI 2026technical0 citations

Generating In-Distribution Counterfactual Explanation for Graph Neural Networks

Linmao Chen, Chaobo He, Junwei Cheng, Chunying Li, Quanlong Guan

Abstract

Graph Neural Networks (GNNs) have received increasing attention due to their ability to handle graph-structured data, yet their explainability remains a significant challenge. An effective solution is to provide the GNN models with counterfactual explanations, which aim to answer “How should the input instance be perturbed to change the model

BibTeX

@inproceedings{aaai2026_generatingindist,
  title = {Generating In-Distribution Counterfactual Explanation for Graph Neural Networks},
  author = {Linmao Chen and Chaobo He and Junwei Cheng and Chunying Li and Quanlong Guan},
  booktitle = {AAAI 2026},
  year = {2026}
}