Cover Image

Unifying Adversarial Perturbation for Graph Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yang, Jinluan, Zhang, Ruihao, Chen, Zhengyu, Wu, Fei, Kuang, Kun
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Leveraging Invariant Principle for Heterophilic Graph Structure Distribution Shifts
by: Yang, Jinluan, et al.
Published: (2024)

Discovering Invariant Neighborhood Patterns for Heterophilic Graphs
by: Yang, Jinluan, et al.
Published: (2024)

Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging
by: Yang, Jinluan, et al.
Published: (2025)

Robust Deep Reinforcement Learning with Adaptive Adversarial Perturbations in Action Space
by: Liu, Qianmei, et al.
Published: (2024)

GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization
by: Yang, Xiaodong, et al.
Published: (2024)

Online Adversarial Knowledge Distillation for Graph Neural Networks
by: Wang, Can, et al.
Published: (2021)

Perturbing the Phase: Analyzing Adversarial Robustness of Complex-Valued Neural Networks
by: Eilers, Florian, et al.
Published: (2026)

Scaling and Transferability of Annealing Strategies in Large Language Model Training
by: Wang, Siqi, et al.
Published: (2025)

Quantifying the Noise of Structural Perturbations on Graph Adversarial Attacks
by: Fang, Junyuan, et al.
Published: (2025)

Enhancing the Resilience of Graph Neural Networks to Topological Perturbations in Sparse Graphs
by: He, Shuqi, et al.
Published: (2024)

ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks
by: Chen, Zhaoliang, et al.
Published: (2024)

Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning
by: Zhao, Ziyu, et al.
Published: (2025)

Mitigating Overfitting in Graph Neural Networks via Feature and Hyperplane Perturbation
by: Choi, Yoonhyuk, et al.
Published: (2022)

Universal Prompt Tuning for Graph Neural Networks
by: Fang, Taoran, et al.
Published: (2022)

Volatility in Certainty (VC): A Metric for Detecting Adversarial Perturbations During Inference in Neural Network Classifiers
by: Hemmati, Vahid, et al.
Published: (2025)

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification
by: Zhang, Lu, et al.
Published: (2025)

Hierarchical Zero-Order Optimization for Deep Neural Networks
by: Cao, Sansheng, et al.
Published: (2026)

Combinatorial Optimization with Automated Graph Neural Networks
by: Liu, Yang, et al.
Published: (2024)

GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks
by: Cui, Canyixing, et al.
Published: (2026)

SGNNBench: A Holistic Evaluation of Spiking Graph Neural Network on Large-scale Graph
by: Zhang, Huizhe, et al.
Published: (2025)

A Unified Kernel for Neural Network Learning
by: Zhang, Shao-Qun, et al.
Published: (2024)

GL-LFGNN:A Global-Local Dual-branch Causal Graph Neural Network Based on Liang-Kleeman Information Flow for EEG Emotion Recognition
by: Wang, Ziyi, et al.
Published: (2026)

GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables
by: Li, Zhengyu, et al.
Published: (2026)

Oversmoothing Alleviation in Graph Neural Networks: A Survey and Unified View
by: Jin, Yufei, et al.
Published: (2024)

Towards Reliable Evaluation of Adversarial Robustness for Spiking Neural Networks
by: Wang, Jihang, et al.
Published: (2025)

SRNN: Spatiotemporal Relational Neural Network for Intuitive Physics Understanding
by: Yang, Fei
Published: (2025)

GraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks
by: Fu, Xingbo, et al.
Published: (2025)

Graph Neural Networks for Learning Equivariant Representations of Neural Networks
by: Kofinas, Miltiadis, et al.
Published: (2024)

GRAIN: Multi-Granular and Implicit Information Aggregation Graph Neural Network for Heterophilous Graphs
by: Zhao, Songwei, et al.
Published: (2025)

Decision-focused Graph Neural Networks for Combinatorial Optimization
by: Liu, Yang, et al.
Published: (2024)

Adaptive Layerwise Perturbation: Unifying Off-Policy Corrections for LLM RL
by: Ye, Chenlu, et al.
Published: (2026)

Interpretation of Neural Networks is Susceptible to Universal Adversarial Perturbations
by: Oskouie, Haniyeh Ehsani, et al.
Published: (2022)

APEX: Probing Neural Networks via Activation Perturbation
by: Ren, Tao, et al.
Published: (2026)

How Worst-Case Are Adversarial Attacks? Linking Adversarial and Perturbation Robustness
by: Rossolini, Giulio
Published: (2026)

AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation
by: Fu, Rong, et al.
Published: (2026)

Generating Universal Adversarial Perturbations for Quantum Classifiers
by: Anil, Gautham, et al.
Published: (2024)

IoT-based Android Malware Detection Using Graph Neural Network With Adversarial Defense
by: Yumlembam, Rahul, et al.
Published: (2025)

Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations
by: González-Santana, Alejandro J., et al.
Published: (2026)

ModelGPT: Unleashing LLM's Capabilities for Tailored Model Generation
by: Tang, Zihao, et al.
Published: (2024)

Trustworthy Graph Neural Networks: Aspects, Methods and Trends
by: Zhang, He, et al.
Published: (2022)