Adversarial robustness in graph neural networks: A Hamiltonian approach

K Zhao, Q Kang, Y Song, R She… - Advances in Neural …, 2024 - proceedings.neurips.cc
Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those
that affect both node features and graph topology. This paper investigates GNNs derived …
被引用次数：32 相关文章

Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach

K Zhao, Q Kang, Y Song, R She, S Wang… - arXiv preprint arXiv …, 2023 - arxiv.org
Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those
that affect both node features and graph topology. This paper investigates GNNs derived …

Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach

K Zhao, Q Kang, Y Song, R She, S Wang… - arXiv e …, 2023 - ui.adsabs.harvard.edu
Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those
that affect both node features and graph topology. This paper investigates GNNs derived …
[PDF] sutd.edu.sg

[PDF][PDF] Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach

Q Kang, Y Song, R She, S Wang, WP Tay - fcp.sutd.edu.sg
Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those
that affect both node features and graph topology. This paper investigates GNNs derived …

Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach

K Zhao, Q Kang, Y Song, R She, S Wang… - Thirty-seventh Conference … - openreview.net
Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those
that affect both node features and graph topology. This paper investigates GNNs derived …

Adversarial robustness in graph neural networks: a hamiltonian approach

K Zhao, Q Kang, Y Song, R She, S Wang… - Proceedings of the 37th …, 2023 - dl.acm.org
Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those
that affect both node features and graph topology. This paper investigates GNNs derived …