arXiv AI
GraphToxin: Reconstructing Full Unlearned Graphs from Graph Unlearning
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arXiv AI
Jun 30
Rethinking Generative Reconstruction Attacks against Graph Neural Network Models
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.
arXiv Machine Learning
Jun 9
Beyond Homophily: Towards Generalized Graph Reconstruction Attack and Defense
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 Machine Learning
Aug 13
Defending against Model Extraction for GNNs with Model Reprogramming
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 Machine Learning
Jun 10
Boosting Graph Robustness Against Backdoor Attacks: An Over-Similarity Perspective
arXiv:2502. 01272v3 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have achieved notable success in tasks such as social and transportation networks.
arXiv AI
Sep 11
Kernel-Complexity Edge Sanitization for Training-Free Defense against Structural Graph Attacks
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.
arXiv Machine Learning
Jun 30
Blackknife: Hard-Label Query-Limited Black-Box Attacks on Heterogeneous Graph Neural Networks
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.