IDEA: Invariant Defense for Graph Adversarial Robustness

25 May 2023

Papers citing "IDEA: Invariant Defense for Graph Adversarial Robustness"

8 / 8 papers shown

Title
Exploring Causal Learning through Graph Neural Networks: An In-depth Review Simi Job Xiaohui Tao Taotao Cai Haoran Xie Lin Li Jianming Yong Qing Li CML AI4CE 38 5 0 25 Nov 2023
Does Invariant Graph Learning via Environment Augmentation Learn Invariance? Yongqiang Chen Yatao Bian Kaiwen Zhou Binghui Xie Bo Han James Cheng OOD 31 34 0 29 Oct 2023
DAD++: Improved Data-free Test Time Adversarial Defense Gaurav Kumar Nayak Inder Khatri Shubham Randive Ruchit Rawal Anirban Chakraborty AAML 21 1 0 10 Sep 2023
Revisiting Robustness in Graph Machine Learning Lukas Gosch Daniel Sturm Simon Geisler Stephan Günnemann AAML OOD 72 21 0 01 May 2023
Adversarial Camouflage for Node Injection Attack on Graphs Shuchang Tao Qi Cao Huawei Shen Yunfan Wu Liang Hou Fei Sun Xueqi Cheng AAML GNN 30 21 0 03 Aug 2022
Elastic Graph Neural Networks Xiaorui Liu W. Jin Yao Ma Yaxin Li Hua Liu Yiqi Wang Ming Yan Jiliang Tang 92 107 0 05 Jul 2021
Robust Optimization as Data Augmentation for Large-scale Graphs Kezhi Kong Bernard Ghanem Mucong Ding Zuxuan Wu Chen Zhu Guohao Li Gavin Taylor Tom Goldstein 106 74 0 19 Oct 2020
Out-of-Distribution Generalization via Risk Extrapolation (REx) David M. Krueger Ethan Caballero J. Jacobsen Amy Zhang Jonathan Binas Dinghuai Zhang Rémi Le Priol Aaron Courville OOD 215 901 0 02 Mar 2020