Topology-aware dynamic co-evolution for robust graph learning

Summary

Researchers propose Topology-Aware Dynamic Co-evolution (TADC), a framework that defends Graph Neural Networks against adversarial perturbations of graph structure by jointly optimizing graph topology and model parameters through validation-guided meta-gradients. The method adds a topology-aware gradient calibration mechanism, localizes vulnerable nodes, and separates reliable edges from potentially adversarial ones before training an attention-based DeepGCN. The authors report that TADC consistently outperforms state-of-the-art methods across attack settings on multiple benchmark datasets, with the largest gains under strong perturbations and high perturbation rates.

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