Adversarially Robust Neural Architecture Search for Graph Neural Networks-Reference-Cited by-同舟云学术

Adversarially Robust Neural Architecture Search for Graph Neural Networks

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
language:
Short-container-title:

Author:

Xie Beini¹,Chang Heng¹,Zhang Ziwei¹,Wang Xin¹,Wang Daixin²,Zhang Zhiqiang²,Ying Rex³,Zhu Wenwu¹

Affiliation:

1. Tsinghua University

2. Ant Group

3. Yale University

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Beijing National Research Center for Information Science and Technology (BNRist)

China Postdoctoral Science Foundation

Publisher

IEEE

Link

Reference55 articles.

2. Autogl: A library for automated graph learning;guan;ICLR 2021 Workshop on Geometrical and Topological Representation Learning,0

3. Inductive representation learning on large graphs;hamilton;Neural Information Processing Systems,2017

4. Single path one-shot neural architecture search with uniform sampling;guo;ECCV,0

5. Adversarial attacks on neural networks for graph data;zügner;ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,0

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

2. Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

3. Evaluating the Adversarial Robustness of Evolutionary Neural Architecture Search;2024 International Conference on Multimedia Analysis and Pattern Recognition (MAPR);2024-08-15