New GNN defense uses self-supervised purifier against adversarial attacks

Researchers have developed a novel self-supervised adversarial purification framework for Graph Neural Networks (GNNs). This new method separates the task of robustness from classification by using a dedicated purifier, GPR-GAE, which is a graph auto-encoder trained with a self-supervised strategy. The GPR-GAE utilizes multiple Generalized PageRank filters to capture diverse structural representations, enabling effective purification and robust defense against adversarial attacks on graph data. AI

IMPACT Introduces a new method to enhance the security and reliability of Graph Neural Networks against malicious perturbations.

RANK_REASON The cluster contains an academic paper detailing a new method for defending Graph Neural Networks against adversarial attacks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Generalized PageRank
• GPR-GAE
• Graph Neural Networks

• paper
• safety

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