Towards an Efficient and General Framework of Robust Training for Graph
Neural Networks

Towards an Efficient and General Framework of Robust Training for Graph Neural Networks

25 February 2020

Caiwen Ding

ArXiv (abs)PDF HTML

Papers citing "Towards an Efficient and General Framework of Robust Training for Graph Neural Networks"

6 / 6 papers shown

Title
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions Lukas Gosch Simon Geisler Daniel Sturm Bertrand Charpentier Daniel Zügner Stephan Günnemann AAML GNN 71 32 0 27 Jun 2023
Learning on Graphs under Label Noise Jingyang Yuan Xiao Luo Yifang Qin Yusheng Zhao Wei Ju Ming Zhang NoLa 78 22 0 14 Jun 2023
Revisiting Robustness in Graph Machine Learning Lukas Gosch Daniel Sturm Simon Geisler Stephan Günnemann AAML OOD 142 23 0 01 May 2023
Are Defenses for Graph Neural Networks Robust? Felix Mujkanovic Simon Geisler Stephan Günnemann Aleksandar Bojchevski OOD AAML 91 59 0 31 Jan 2023
Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation Jun Zhuang M. Hasan 69 20 0 21 Aug 2022
Reliable Graph Neural Network Explanations Through Adversarial Training Donald Loveland Shusen Liu B. Kailkhura A. Hiszpanski Yong Han AAML 49 4 0 25 Jun 2021