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Graph Neural Network Explanations are Fragile.

Published in ICML 2024, 2024

This paper studies the vulnerability of GNN explanations.

Recommended citation: Jiate Li, Meng Pang, Yun Dong, Jinyuan Jia, Binghui Wang. (2024). "Graph Neural Network Explanations are Fragile." ICML 2024.
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Practicable Black-box Evasion Attacks on Link Prediction in Dynamic Graphs–A Graph Sequential Embedding Method

Published in AAAI 2025, 2024

This paper proposed a practical and effective black-box attack against LPDG.

Recommended citation: Jiate Li, Meng Pang, Binghui Wang. (2025). "Practicable Black-box Evasion Attacks on Link Prediction in Dynamic Graphs--A Graph Sequential Embedding Method." AAAI 2025.
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AGNNCert: Defending Graph Neural Networks against Arbitrary Perturbations with Deterministic Certification

Published in Usenix Security 2025, 2025

This paper proposed an effective certifiable robust GNN method against arbitary perturbations

Recommended citation: Jiate Li, Binghui Wang. (2025). "AGNNCert: Defending Graph Neural Networks against Arbitrary Perturbations with Deterministic Certification." Usenix Security 2025.
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Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks

Published in ICLR 2025, 2025

This paper proposed a provably robust framework for GNN explainers.

Recommended citation: Jiate Li, Meng Pang, Yun Dong, Jinyuan Jia, Binghui Wang. (2025). "Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks." ICLR 2025.
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Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations

Published in CVPR 2025, 2025

This paper extends AGNNCert to defending posioning attack.

Recommended citation: Jiate Li, Meng Pang, Yun Dong, Binghui Wang. (2025). "Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations." CVPR 2025.
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