DefenseVGAE: Defending against Adversarial Attacks on Graph Data via a
Variational Graph Autoencoder

DefenseVGAE: Defending against Adversarial Attacks on Graph Data via a Variational Graph Autoencoder

16 June 2020

Ao Zhang

Papers citing "DefenseVGAE: Defending against Adversarial Attacks on Graph Data via a Variational Graph Autoencoder"

6 / 6 papers shown

Title
When Witnesses Defend: A Witness Graph Topological Layer for Adversarial Graph Learning Naheed Anjum Arafat D. Basu Yulia R. Gel Yuzhou Chen AAML 163 0 0 21 Sep 2024
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks Yan Scholten Jan Schuchardt Simon Geisler Aleksandar Bojchevski Stephan Günnemann AAML 29 15 0 05 Jan 2023
Trustworthy Graph Neural Networks: Aspects, Methods and Trends He Zhang Bang Wu Xingliang Yuan Shirui Pan Hanghang Tong Jian Pei 45 104 0 16 May 2022
Graph Data Augmentation for Graph Machine Learning: A Survey Tong Zhao Wei Jin Yozen Liu Yingheng Wang Gang Liu Stephan Günnemann Neil Shah Meng Jiang OOD 30 79 0 17 Feb 2022
Data Augmentation for Deep Graph Learning: A Survey Kaize Ding Zhe Xu Hanghang Tong Huan Liu OOD GNN 27 219 0 16 Feb 2022
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection Yulin Zhu Y. Lai Kaifa Zhao Xiapu Luo Ming Yuan Jian Ren Kai Zhou AAML 33 24 0 18 Jun 2021