{"data":{"id":"8ea329fb-234a-4425-88ac-29e4ba28f512","title":"Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimization","summary":"This paper proposes AT-GSE, an adversarial training method for graph neural networks that generates perturbed graphs by maximizing a graph subspace energy (GSE) regularization term, a generalization of graph energy that measures the stability of the adjacency matrix. The authors use randomized SVD and Nyström low-rank approximation to handle local and global topology perturbations from LRBCD and PRBCD. Experiments across homophily and heterophily datasets show AT-GSE outperforms prior GNN adversarial training methods in adversarial accuracy and also achieves higher clean accuracy on unperturbed graphs.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11400575","publishedAt":"2026-02-19T13:16:26.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["other"],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-02-19T13:16:26.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.9,"researchCategory":"peer_reviewed","atlasIds":null}}