Papers › AGNNCert: Defending Graph Neural Networks against Arbitrary Perturbations with...
AGNNCert: Defending Graph Neural Networks against Arbitrary Perturbations with Deterministic Certification
Jiate Li, Binghui Wang
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Graph neural networks (GNNs) achieve the state-of-the-art on graph-relevant tasks such as node and graph classification. However, recent works show GNNs are vulnerable to adversarial perturbations include the perturbation on edges, nodes, and node features, the three components forming a graph. Empirical defenses against such attacks are soon broken by adaptive ones. While certified defenses offer robustness guarantees, they face several limitations: 1) almost all restrict the adversary's capability to only one type of perturbation, which is impractical; 2) all are designed for a particular GNN task, which limits their applicability; and 3) the robustness guarantees of all methods except one are not 100% accurate. We address all these limitations by developing AGNNCert, the first certified defense for GNNs against arbitrary (edge, node, and node feature) perturbations with deterministic robustness guarantees, and applicable to the two most common node and graph classification tasks. AGNNCert also encompass existing certified defenses as special cases. Extensive evaluations on multiple benchmark node/graph classification datasets and two real-world graph datasets, and multiple GNNs validate the effectiveness of AGNNCert to provably defend against arbitrary perturbations. AGNNCert also shows its superiority over the state-of-the-art certified defenses against the individual edge perturbation and node perturbation.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections