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:2503. 00065v4 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) achieve high performance in various real-world applications, such as drug discovery, traffic states prediction, and recommendation systems.
By Jing Xu, Franziska Boenisch, Adam Dziedzic
arXiv:2608. 11495v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) serve as the backbone for high-stakes applications in Machine-Learning-as-a-Service (MLaaS).
By Yan Wen, Zhenyi Wang, Heng Huang
arXiv:2606. 29748v1 Announce Type: new Abstract: The application of graph data in numerous disciplines raises the need for gathering and analyzing huge volumes of data, some of which is private and sensitive.
By Adebayo Keji, Sayanton Dibbo
arXiv:2606. 08067v1 Announce Type: new Abstract: Graph neural networks (GNNs) are widely deployed on relational data, yet they can leak sensitive or proprietary information about the training graph adjacency, e.
By Zhanke Zhou, Bo Han, Xuan Li, Jiangchao Yao, Sanmi Koyejo, Michael K. Ng
arXiv:2608.29054v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) have emerged as a cornerstone for representing complex relational dependencies in diverse multimedia tasks, particularly i...
By Shuomin Xue, Jingyuan Li, Ju Jia, Jingxuan Yu, Xiaojun Jia
arXiv:2606. 25589v1 Announce Type: new Abstract: As graph neural networks (GNNs) become standard tools for critical tasks in circuit design and analysis, their security and privacy risks require careful attention.
By Rupesh Raj Karn, Johann Knechtel, Ozgur Sinanoglu
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:2607. 07089v1 Announce Type: new Abstract: Relational Deep Learning (RDL) has become a standard methodology for machine learning on relational databases: the database is encoded as a heterogeneous temporal graph in which tuples become nodes and primary-key to foreign-key (PK-FK) dependencies become typed edges, over which a graph neural network is trained for downstream prediction.
By Alan Gany, Bogdan Cautis, Silviu Maniu
arXiv:2609.37972v1 Announce Type: cross
Abstract: As Graph Neural Networks (GNNs) are widely deployed as Machine Learning-as-a-Service (MLaaS) APIs, model stealing attacks have emerged as a critical...
By Ying Song, Xiaowei Jia, Balaji Palanisamy
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
Relational Deep Learning (RDL) has become a standard methodology for machine learning on relational databases: the database is encoded as a heterogeneous temporal graph in which tuples become nodes and primary-key to foreign-key (PK-FK) dependencies become typed edges, over which a graph neural network is trained for downstream prediction. We study the adversarial robustness of this pipeline.