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Topology Attack and Defense for Graph Neural Networks: An Optimization
  Perspective

Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective

10 June 2019
Kaidi Xu
Hongge Chen
Sijia Liu
Pin-Yu Chen
Tsui-Wei Weng
Mingyi Hong
Xue Lin
    AAML
ArXivPDFHTML

Papers citing "Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective"

50 / 90 papers shown
Title
Adverseness vs. Equilibrium: Exploring Graph Adversarial Resilience through Dynamic Equilibrium
Adverseness vs. Equilibrium: Exploring Graph Adversarial Resilience through Dynamic Equilibrium
Xinxin Fan
Wenxiong Chen
Mengfan Li
Wenqi Wei
Ling Liu
AAML
19
0
0
20 May 2025
Informed, but Not Always Improved: Challenging the Benefit of Background Knowledge in GNNs
Informed, but Not Always Improved: Challenging the Benefit of Background Knowledge in GNNs
Kutalmış Coşkun
Ivo Kavisanczki
Amin Mirzaei
Tom Siegl
Bjarne C. Hiller
Stefan Lüdtke
Martin Becker
17
0
0
16 May 2025
Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense
Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense
Kirill Lukyanov
Mikhail Drobyshevskiy
Georgii Sazonov
Mikhail Soloviov
Ilya Makarov
GNN
59
0
0
06 May 2025
Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks
Xuewen Dong
Jiachen Li
Shujun Li
Zhichao You
Qiang Qu
Yaroslav Kholodov
Yulong Shen
AAML
48
0
0
12 Mar 2025
VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learning
VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learning
Yang Chen
Bin Zhou
AAML
FedML
45
0
0
24 Feb 2025
Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective
Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective
Chang Liu
Hai Huang
Yujie Xing
Xingquan Zuo
AAML
54
0
0
04 Feb 2025
When Witnesses Defend: A Witness Graph Topological Layer for Adversarial Graph Learning
When Witnesses Defend: A Witness Graph Topological Layer for Adversarial Graph Learning
Naheed Anjum Arafat
D. Basu
Yulia R. Gel
Yuzhou Chen
AAML
244
0
0
21 Sep 2024
Debiased Graph Poisoning Attack via Contrastive Surrogate Objective
Debiased Graph Poisoning Attack via Contrastive Surrogate Objective
Kanghoon Yoon
Yeonjun In
Namkyeong Lee
Kibum Kim
Chanyoung Park
AAML
36
2
0
27 Jul 2024
RIDA: A Robust Attack Framework on Incomplete Graphs
RIDA: A Robust Attack Framework on Incomplete Graphs
Jianke Yu
Hanchen Wang
Chen Chen
Xiaoyang Wang
Wenjie Zhang
Ying Zhang
Ying Zhang
Xijuan Liu
GNN
OOD
AAML
50
1
0
25 Jul 2024
Explainable Graph Neural Networks Under Fire
Explainable Graph Neural Networks Under Fire
Zhong Li
Simon Geisler
Yuhang Wang
Stephan Günnemann
M. Leeuwen
AAML
48
1
0
10 Jun 2024
Efficient Topology-aware Data Augmentation for High-Degree Graph Neural
  Networks
Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks
Dongrui Fan
Xiaoyang Lin
Renchi Yang
Hongtao Wang
46
2
0
08 Jun 2024
Link Stealing Attacks Against Inductive Graph Neural Networks
Link Stealing Attacks Against Inductive Graph Neural Networks
Yixin Wu
Xinlei He
Pascal Berrang
Mathias Humbert
Michael Backes
Neil Zhenqiang Gong
Yang Zhang
47
2
0
09 May 2024
Simplified PCNet with Robustness
Simplified PCNet with Robustness
Bingheng Li
Xuanting Xie
Haoxiang Lei
Ruiyi Fang
Zhao Kang
45
5
0
06 Mar 2024
Attacking Large Language Models with Projected Gradient Descent
Attacking Large Language Models with Projected Gradient Descent
Simon Geisler
Tom Wollschlager
M. H. I. Abdalla
Johannes Gasteiger
Stephan Günnemann
AAML
SILM
49
50
0
14 Feb 2024
Effective backdoor attack on graph neural networks in link prediction tasks
Effective backdoor attack on graph neural networks in link prediction tasks
Jiazhu Dai
Haoyu Sun
GNN
63
3
0
05 Jan 2024
Poisoning $\times$ Evasion: Symbiotic Adversarial Robustness for Graph
  Neural Networks
Poisoning ×\times× Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks
Ege Erdogan
Simon Geisler
Stephan Günnemann
AAML
37
0
0
09 Dec 2023
Deceptive Fairness Attacks on Graphs via Meta Learning
Deceptive Fairness Attacks on Graphs via Meta Learning
Jian Kang
Yinglong Xia
Ross Maciejewski
Jiebo Luo
Yangqiu Song
44
4
0
24 Oct 2023
Adversarial Attacks on Fairness of Graph Neural Networks
Adversarial Attacks on Fairness of Graph Neural Networks
Binchi Zhang
Yushun Dong
Chen Chen
Yada Zhu
Minnan Luo
Jundong Li
55
3
0
20 Oct 2023
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks
Haoran Liu
Bokun Wang
Jianling Wang
Xiangjue Dong
Tianbao Yang
James Caverlee
AAML
44
3
0
29 Aug 2023
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and
  New Directions
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
30
29
0
27 Jun 2023
Revisiting Robustness in Graph Machine Learning
Revisiting Robustness in Graph Machine Learning
Lukas Gosch
Daniel Sturm
Simon Geisler
Stephan Günnemann
AAML
OOD
80
22
0
01 May 2023
Towards Reasonable Budget Allocation in Untargeted Graph Structure
  Attacks via Gradient Debias
Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias
Zihan Liu
Yun Luo
Lirong Wu
Zicheng Liu
Stan Z. Li
AAML
30
25
0
29 Mar 2023
Decentralized Adversarial Training over Graphs
Decentralized Adversarial Training over Graphs
Ying Cao
Elsa Rizk
Stefan Vlaski
Ali H. Sayed
AAML
50
1
0
23 Mar 2023
It Is All About Data: A Survey on the Effects of Data on Adversarial
  Robustness
It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness
Peiyu Xiong
Michael W. Tegegn
Jaskeerat Singh Sarin
Shubhraneel Pal
Julia Rubin
SILM
AAML
37
8
0
17 Mar 2023
Multi-Agent Adversarial Training Using Diffusion Learning
Multi-Agent Adversarial Training Using Diffusion Learning
Ying Cao
Elsa Rizk
Stefan Vlaski
Ali H. Sayed
DiffM
42
4
0
03 Mar 2023
Unnoticeable Backdoor Attacks on Graph Neural Networks
Unnoticeable Backdoor Attacks on Graph Neural Networks
Enyan Dai
Minhua Lin
Xiang Zhang
Suhang Wang
AAML
31
47
0
11 Feb 2023
Are Defenses for Graph Neural Networks Robust?
Are Defenses for Graph Neural Networks Robust?
Felix Mujkanovic
Simon Geisler
Stephan Günnemann
Aleksandar Bojchevski
OOD
AAML
21
59
0
31 Jan 2023
Randomized Message-Interception Smoothing: Gray-box Certificates for
  Graph Neural Networks
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks
Yan Scholten
Jan Schuchardt
Simon Geisler
Aleksandar Bojchevski
Stephan Günnemann
AAML
29
16
0
05 Jan 2023
Adversarial Weight Perturbation Improves Generalization in Graph Neural
  Networks
Adversarial Weight Perturbation Improves Generalization in Graph Neural Networks
Yihan Wu
Aleksandar Bojchevski
Heng Huang
AAML
44
30
0
09 Dec 2022
Robust Graph Representation Learning via Predictive Coding
Robust Graph Representation Learning via Predictive Coding
Billy Byiringiro
Tommaso Salvatori
Thomas Lukasiewicz
OOD
31
6
0
09 Dec 2022
Spectral Adversarial Training for Robust Graph Neural Network
Spectral Adversarial Training for Robust Graph Neural Network
Jintang Li
Jiaying Peng
Liang Chen
Zibin Zheng
Tingting Liang
Qing Ling
AAML
OOD
33
19
0
20 Nov 2022
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node
  Classification
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification
Yulin Zhu
Liang Tong
Gaolei Li
Xiapu Luo
Kai Zhou
35
4
0
25 Oct 2022
Empowering Graph Representation Learning with Test-Time Graph
  Transformation
Empowering Graph Representation Learning with Test-Time Graph Transformation
Wei Jin
Tong Zhao
Jiayu Ding
Yozen Liu
Jiliang Tang
Neil Shah
OOD
86
62
0
07 Oct 2022
Model Inversion Attacks against Graph Neural Networks
Model Inversion Attacks against Graph Neural Networks
Zaixin Zhang
Qi Liu
Zhenya Huang
Hao Wang
Cheekong Lee
Enhong
AAML
28
35
0
16 Sep 2022
What Does the Gradient Tell When Attacking the Graph Structure
What Does the Gradient Tell When Attacking the Graph Structure
Zihan Liu
Ge Wang
Yun Luo
Stan Z. Li
AAML
27
2
0
26 Aug 2022
Reliable Representations Make A Stronger Defender: Unsupervised
  Structure Refinement for Robust GNN
Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN
Kuan Li
Yang Liu
Xiang Ao
Jianfeng Chi
Jinghua Feng
Hao Yang
Qing He
AAML
51
64
0
30 Jun 2022
Transferable Graph Backdoor Attack
Transferable Graph Backdoor Attack
Shuiqiao Yang
Bao Gia Doan
Paul Montague
O. Vel
Tamas Abraham
S. Çamtepe
Damith C. Ranasinghe
S. Kanhere
AAML
49
36
0
21 Jun 2022
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural
  Networks
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks
Runlin Lei
Zhen Wang
Yaliang Li
Bolin Ding
Zhewei Wei
AAML
35
43
0
27 May 2022
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and
  Privacy Protection
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Bingzhe Wu
Jintang Li
Junchi Yu
Yatao Bian
Hengtong Zhang
...
Guangyu Sun
Peng Cui
Zibin Zheng
Zhe Liu
P. Zhao
OOD
45
25
0
20 May 2022
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
He Zhang
Bang Wu
Xingliang Yuan
Shirui Pan
Yangqiu Song
Jian Pei
47
104
0
16 May 2022
Bandits for Structure Perturbation-based Black-box Attacks to Graph
  Neural Networks with Theoretical Guarantees
Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees
Binghui Wang
Youqin Li
Pan Zhou
AAML
34
14
0
07 May 2022
Detecting Topology Attacks against Graph Neural Networks
Detecting Topology Attacks against Graph Neural Networks
Senrong Xu
Yuan Yao
Liangyue Li
Wei Yang
F. Xu
Yangqiu Song
47
3
0
21 Apr 2022
GUARD: Graph Universal Adversarial Defense
GUARD: Graph Universal Adversarial Defense
Jintang Li
Jie Liao
Ruofan Wu
Liang Chen
Zibin Zheng
Jiawang Dan
Changhua Meng
Weiqiang Wang
AAML
33
8
0
20 Apr 2022
Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with
  Heterophily
Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily
Jie Chen
Shouzhen Chen
Junbin Gao
Zengfeng Huang
Junping Zhang
Jian Pu
54
24
0
19 Mar 2022
Model-Agnostic Augmentation for Accurate Graph Classification
Model-Agnostic Augmentation for Accurate Graph Classification
Jaemin Yoo
Sooyeon Shim
U. Kang
GNN
29
29
0
21 Feb 2022
Graph Data Augmentation for Graph Machine Learning: A Survey
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
39
80
0
17 Feb 2022
Holistic Adversarial Robustness of Deep Learning Models
Holistic Adversarial Robustness of Deep Learning Models
Pin-Yu Chen
Sijia Liu
AAML
54
16
0
15 Feb 2022
Recent Advances in Reliable Deep Graph Learning: Inherent Noise,
  Distribution Shift, and Adversarial Attack
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack
Jintang Li
Bingzhe Wu
Chengbin Hou
Guoji Fu
Yatao Bian
Liang Chen
Junzhou Huang
Zibin Zheng
OOD
AAML
44
6
0
15 Feb 2022
Toward Enhanced Robustness in Unsupervised Graph Representation
  Learning: A Graph Information Bottleneck Perspective
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective
Jihong Wang
Minnan Luo
Jundong Li
Ziqi Liu
Jun Zhou
Qinghua Zheng
AAML
23
5
0
21 Jan 2022
Unsupervised Graph Poisoning Attack via Contrastive Loss
  Back-propagation
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation
Sixiao Zhang
Hongxu Chen
Xiangguo Sun
Yicong Li
Guandong Xu
AAML
SSL
25
42
0
20 Jan 2022
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