arXiv:2607. 04600v1 Announce Type: new Abstract: Graph eXplainable AI (G-XAI) is increasingly important for making Graph Neural Networks interpretable and accountable.
By Francesco Paolo Nerini, Mirko Zaffaroni, Paolo Baracco, Gabriele Ciravegna, Alan Perotti
Graph eXplainable AI (G-XAI) is increasingly important for making Graph Neural Networks interpretable and accountable. While a growing number of explainers are available, choosing the right method and assessing the trustworthiness of its outputs remains unclear.
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
Graph Machine Learning as a Service platforms now offer explainability interfaces to satisfy regulatory transparency, but this transparency can be exploited. The paper introduces a novel model extraction attack for graph classification that operates under strict black‑box constraints, using only discrete class labels and binary explanation masks. The method guides Monte Carlo edge sensitivity estimation toward decision boundaries with Hoeffding guarantees and narrows the search space using explanation subgraphs, outperforming comparable baselines on benchmark datasets.
By Ojas Nimase, Jiate Li, Yue Zhao, Yushun Dong
arXiv:2607. 21381v1 Announce Type: new Abstract: Instance-level explanations aim to reveal the rationale behind a model's decisions for a specific graph.
By Jiancu Chen, Shuyin Xia, Guan Wang, Degang Chen, Fan Chen
Instance-level explanations aim to reveal the rationale behind a model's decisions for a specific graph. Previous methods explain graph neural networks (GNNs) by selecting important edges to induce subgraphs, where edge importance is assessed by perturbing each edge and observing changes in the model predictions.