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).
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.
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...
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.
Kernel-Complexity Edge Sanitization (KCES) is a training‑free, model‑agnostic defense for Graph Neural Networks that identifies and removes edges with high Kernel‑Complexity (KC) scores, which are indicative of structural influence on the graph kernel complexity metric. KCES leverages a theoretical upper bound on GNN test error derived from the graph Gram matrix to compute edge‑specific KC scores, pruning edges that are empirically enriched with adversarial perturbations. The method is computationally efficient, scalable to large graphs, and consistently outperforms representative robust baselines across diverse attack settings without requiring retraining.
Blackknife is a query‑free, strict black‑box evasion attack designed for heterogeneous graph neural networks (HGNNs). It relies solely on observing one‑hop structures, ground‑truth labels, and raw features of target nodes, without using victim‑model outputs. The attack trains a shared surrogate on observable edges and relation types, then optimizes structural perturbations via continuous relaxation and projected gradient ascent, preserving node degrees while adding a cosine‑similarity penalty and adaptive structural classifier for regularization. Experiments show competitive success rates across multiple victim architectures and defense settings, revealing HGNN vulnerabilities under restricted graph access and query‑free attack generation.
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.
arXiv:2607. 18567v1 Announce Type: new Abstract: A graph foundation model generalizes across graph domains by mapping every input into one shared representation before any task reasoning.
arXiv:2606. 29240v1 Announce Type: new Abstract: Heterogeneous graph neural networks (HGNNs) have achieved strong performance in modeling complex graph-structured data with multiple node and relation types.
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.
The paper investigates whether spiking neural networks (SNNs) are more resistant to model stealing attacks than conventional artificial neural networks (ANNs). By benchmarking four common attacks on MNIST, Fashion‑MNIST, and CIFAR‑10, the authors find that SNNs consistently yield surrogates with 10%–20% lower agreement and 15%–25% lower adversarial transferability compared to ANNs. The authors attribute this resilience to the spike‑based, time‑dependent dynamics of SNNs, which make their decision boundaries harder for ANN‑based surrogates to approximate.
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...