The paper presents a comparative analysis of six state‑of‑the‑art counterfactual explainers for graph neural networks, focusing on methods that can both add and remove edges to alter model predictions. It evaluates these explainers across diverse real‑world and synthetic datasets, covering binary and multi‑class graph and node classification tasks, using a range of quantitative and qualitative metrics. The study highlights the trade‑offs between explanation size, coverage, and quality, aiming to pinpoint each method’s strengths and weaknesses to inform future research.
By Maria Myrto Villia, Filippos Gouidis, Theodore Patkos, Panos Trahanias
arXiv:2606. 05756v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable performance across a range of applications involving graph-structured data, particularly in high-stakes domains.
By Jialiang Yin, Zheng Zhao, Linsey Pang, Bo Dong, Bin Shi, Jiaxing Zhang
arXiv:2608.23835v1 Announce Type: new
Abstract: Real-world decision-making in public health and social science can greatly benefit from predictive models, yet translating predictions into effective i...
By Mulin Tian, Ajitesh Srivastava
WOMBAT is a benchmark comprising 14 whitebox graph neural networks (GNNs) whose message‑passing weights are manually set to detect specific SMARTS motifs. Each model’s decision rule is explicitly known, providing a ground truth for attribution that allows researchers to identify and study errors in post‑hoc explainers such as GNNExplainer, PGExplainer, and Integrated Gradients. The authors validate the models on millions of PubChem molecules, demonstrate how Integrated Gradients can be misled to spread attribution, and release the dataset, models, and evaluation code for future XAI tool development.
By Dominik Matuszek, Bartosz Zieli\'nski, Tomasz Danel, Dawid Rymarczyk
arXiv:2609.05694v1 Announce Type: new
Abstract: Predicting olfactory qualities from molecular structure is an open problem in chemoinformatics. Although linear models can link molecular features to o...
By Mrityunjay Sharma, Sarabeshwar Balaji, Valentina Parma, Ritesh Kumar
arXiv:2506. 03087v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have become essential tools for analyzing graph-structured data in domains such as drug discovery and financial analysis, leading to a growing demand for model transparency.
By Bin Ma, Yuyuan Feng, Minhua Lin, Enyan Dai