Image de couverture de livre

Adversarial Attacks on Fairness of Graph Neural Networks

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Zhang, Binchi, Dong, Yushun, Chen, Chen, Zhu, Yada, Luo, Minnan, Li, Jundong
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Certified Defense on the Fairness of Graph Neural Networks
par: Dong, Yushun, et autres
Publié: (2023)

Towards Certified Unlearning for Deep Neural Networks
par: Zhang, Binchi, et autres
Publié: (2024)

IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks
par: Dong, Yushun, et autres
Publié: (2024)

Federated Graph Learning with Graphless Clients
par: Fu, Xingbo, et autres
Publié: (2024)

GraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks
par: Fu, Xingbo, et autres
Publié: (2025)

Safety in Graph Machine Learning: Threats and Safeguards
par: Wang, Song, et autres
Publié: (2024)

Federated Graph Learning with Structure Proxy Alignment
par: Fu, Xingbo, et autres
Publié: (2024)

A Benchmark for Fairness-Aware Graph Learning
par: Dong, Yushun, et autres
Publié: (2024)

Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective
par: Dong, Yushun, et autres
Publié: (2024)

Verification of Machine Unlearning is Fragile
par: Zhang, Binchi, et autres
Publié: (2024)

Beyond the Permutation Symmetry of Transformers: The Role of Rotation for Model Fusion
par: Zhang, Binchi, et autres
Publié: (2025)

Adaptive Dual Prompting: Hierarchical Debiasing for Fairness-aware Graph Neural Networks
par: Yang, Yuhan, et autres
Publié: (2025)

Rethinking Fair Graph Neural Networks from Re-balancing
par: Li, Zhixun, et autres
Publié: (2024)

Tackling the Local Bias in Federated Graph Learning
par: Zhang, Binchi, et autres
Publié: (2021)

ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data
par: Lei, Zhenyu, et autres
Publié: (2024)

Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections
par: Luo, Zihan, et autres
Publié: (2024)

Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning
par: Fu, Xingbo, et autres
Publié: (2024)

CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models
par: Wang, Song, et autres
Publié: (2024)

FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs
par: Chen, Zihan, et autres
Publié: (2025)

Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?
par: Nimase, Ojas, et autres
Publié: (2026)

ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks
par: Cheng, Zhan, et autres
Publié: (2025)

Reasoning of Large Language Models over Knowledge Graphs with Super-Relations
par: Wang, Song, et autres
Publié: (2025)

ARIEL: Adversarial Graph Contrastive Learning
par: Feng, Shengyu, et autres
Publié: (2022)

GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization
par: Yang, Xiaodong, et autres
Publié: (2024)

Edge Prompt Tuning for Graph Neural Networks
par: Fu, Xingbo, et autres
Publié: (2025)

Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
par: Zhang, Jiahao, et autres
Publié: (2026)

Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective
par: Wang, Senmiao, et autres
Publié: (2025)

CREDIT: Certified Ownership Verification of Deep Neural Networks Against Model Extraction Attacks
par: Shen, Bolin, et autres
Publié: (2026)

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding
par: Zhu, Yuchang, et autres
Publié: (2025)

AHSG: Adversarial Attack on High-level Semantics in Graph Neural Networks
par: Yuan, Kai, et autres
Publié: (2024)

Spectral Greedy Coresets for Graph Neural Networks
par: Ding, Mucong, et autres
Publié: (2024)

Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation
par: Ngo, Tran Gia Bao, et autres
Publié: (2026)

Adversarial Attacks on Locally Private Graph Neural Networks
par: Varun, Matta, et autres
Publié: (2026)

One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes
par: Zhu, Yuchang, et autres
Publié: (2024)

Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction
par: He, Yinhan, et autres
Publié: (2024)

Towards Quantifying the Hessian Structure of Neural Networks
par: Dong, Zhaorui, et autres
Publié: (2025)

MolEdit: Knowledge Editing for Multimodal Molecule Language Models
par: Lei, Zhenyu, et autres
Publié: (2025)

Adversarial Attacks on Graph Neural Networks via Meta Learning
par: Zügner, Daniel, et autres
Publié: (2019)

FairACE: Achieving Degree Fairness in Graph Neural Networks via Contrastive and Adversarial Group-Balanced Training
par: Liu, Jiaxin, et autres
Publié: (2025)

Unifying Adversarial Perturbation for Graph Neural Networks
par: Yang, Jinluan, et autres
Publié: (2025)