Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies

2 March 2020

Papers citing "Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies"

9 / 9 papers shown

Title
You Can't Ignore Either: Unifying Structure and Feature Denoising for Robust Graph Learning Tianmeng Yang Jiahao Meng Min Zhou Yaming Yang Yujing Wang Xiangtai Li Yunhai Tong NoLa 19 1 0 01 Aug 2024
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems Guangjing Wang Ce Zhou Yuanda Wang Bocheng Chen Hanqing Guo Qiben Yan AAML SILM 51 3 0 20 Nov 2023
Trustworthy Graph Neural Networks: Aspects, Methods and Trends He Zhang Bang Wu Xingliang Yuan Shirui Pan Hanghang Tong Jian Pei 41 98 0 16 May 2022
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation Sixiao Zhang Hongxu Chen Xiangguo Sun Yicong Li Guandong Xu AAML SSL 12 42 0 20 Jan 2022
Model Stealing Attacks Against Inductive Graph Neural Networks Yun Shen Xinlei He Yufei Han Yang Zhang 14 60 0 15 Dec 2021
Decentralized Inference with Graph Neural Networks in Wireless Communication Systems Mengyuan Lee Guanding Yu H. Dai GNN 33 39 0 19 Apr 2021
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks Xiang Zhang Marinka Zitnik AAML 11 286 0 15 Jun 2020
DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses Yaxin Li Wei Jin Han Xu Jiliang Tang AAML 9 128 0 13 May 2020
Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing Jinyuan Jia Binghui Wang Xiaoyu Cao Neil Zhenqiang Gong AAML 79 81 0 09 Feb 2020