Graph Adversarial Defense with Virtual Spectral Anchor Injection

Graph Neural Networks (GNNs) have demonstrated superior performance in various graph learning tasks such as node classification and link prediction. However, recent studies have shown that GNNs are highly vulnerable to graph adversarial attacks. To enhance the robustness of GNNs, existing defense strategies have primarily focused on adversarial training on graph data, graph purification, or certifiable robustness. Different from the conventional defense paradigms, we propose Anchor Node Injection Enhancement (ANIE), an active paradigm that repurposes the offensive tactic of node injection into a structural defense. ANIE injects virtual ''auxiliary anchor nodes'' to stabilize the graph's spectral manifold through Dirichlet energy minimization. By establishing robust class prototypes, ANIE reinforces class-wise structural stability and guides perturbed nodes back to correct manifolds. As an attack-agnostic and ''plug-and-play'' framework, ANIE requires no physical modification of the original graph and generalizes to both transductive and inductive settings. Empirical results across multiple benchmarks demonstrate that ANIE significantly enhances GNN robustness, outperforming state-of-the-art defenses by up to 2× in classification accuracy under poisoning and evasion attacks.