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The Confidence Trap: Calibration Attacks for Graph Neural Networks
Cuong Dang, Jiahao Zhang, Hieu Ta Quang +3
While confidence calibration is essential for trustworthy decision-making in safety-critical applications, the robustness of calibrated GNNs to adversarial structural perturbations…
Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
Jiahao Zhang, Yilong Wang, Suhang Wang
Graph neural networks (GNNs) are widely used for learning from graph-structured data in domains such as social networks, recommender systems, and financial platforms. To comply wit…
Unlearning Inversion Attacks for Graph Neural Networks
Jiahao Zhang, Yilong Wang, Zhiwei Zhang +2
Graph unlearning methods aim to efficiently remove the impact of sensitive data from trained GNNs without full retraining, assuming that deleted information cannot be recovered. In…
Enhance GNNs with Reliable Confidence Estimation via Adversarial Calibration Learning
Yilong Wang, Jiahao Zhang, Tianxiang Zhao +1
Despite their impressive predictive performance, GNNs often exhibit poor confidence calibration, i.e., their predicted confidence scores do not accurately reflect true correctness…