Cover Image

Understanding the Robustness of Graph Neural Networks against Adversarial Attacks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wu, Tao, Cui, Canyixing, Xian, Xingping, Qiao, Shaojie, Wang, Chao, Yuan, Lin, Yu, Shui
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Social and Information Networks
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning
by: Wu, Tao, et al.
Published: (2024)

GISExplainer: On Explainability of Graph Neural Networks via Game-theoretic Interaction Subgraphs
by: Xian, Xingping, et al.
Published: (2024)

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

Disentangled Graph Representation Based on Substructure-Aware Graph Optimal Matching Kernel Convolutional Networks
by: Wang, Mao, et al.
Published: (2025)

Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks
by: Huang, Zhenhua, et al.
Published: (2024)

Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?
by: Zhang, Zhongjian, et al.
Published: (2024)

Attacks on Node Attributes in Graph Neural Networks
by: Xu, Ying, et al.
Published: (2024)

InfraredGP: Efficient Graph Partitioning via Spectral Graph Neural Networks with Negative Corrections
by: Qin, Meng, et al.
Published: (2025)

Topologically-Stabilized Graph Neural Networks: Empirical Robustness Across Domains
by: Losic, Jelena
Published: (2025)

Demystifying Oversmoothing in Attention-Based Graph Neural Networks
by: Wu, Xinyi, et al.
Published: (2023)

A Unified Graph Selective Prompt Learning for Graph Neural Networks
by: Jiang, Bo, et al.
Published: (2024)

Conditional Local Feature Encoding for Graph Neural Networks
by: Wang, Yongze, et al.
Published: (2024)

Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification
by: Xie, Beini, et al.
Published: (2024)

Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields
by: Abbahaddou, Yassine, et al.
Published: (2024)

Incorporating Heterophily into Graph Neural Networks for Graph Classification
by: Yang, Jiayi, et al.
Published: (2022)

Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs
by: Lee, Dongjin, et al.
Published: (2023)

Forward Learning of Graph Neural Networks
by: Park, Namyong, et al.
Published: (2024)

Large Language Models for Link Stealing Attacks Against Graph Neural Networks
by: Guan, Faqian, et al.
Published: (2024)

Disambiguated Node Classification with Graph Neural Networks
by: Zhao, Tianxiang, et al.
Published: (2024)

Heterogeneous Graph Neural Network on Semantic Tree
by: Guan, Mingyu, et al.
Published: (2024)

Graph Neural Networks with Diverse Spectral Filtering
by: Guo, Jingwei, et al.
Published: (2023)

Feature Selection and Extraction for Graph Neural Networks
by: Acharya, Deepak Bhaskar, et al.
Published: (2019)

Heterophilous Distribution Propagation for Graph Neural Networks
by: Zheng, Zhuonan, et al.
Published: (2024)

What Is Missing In Homophily? Disentangling Graph Homophily For Graph Neural Networks
by: Zheng, Yilun, et al.
Published: (2024)

Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction
by: Lu, Kangkang, et al.
Published: (2024)

Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems
by: Senapati, Aadarsh, et al.
Published: (2026)

Link Prediction with Physics-Inspired Graph Neural Networks
by: Di Francesco, Andrea Giuseppe, et al.
Published: (2024)

Manufacturing Service Capability Prediction with Graph Neural Networks
by: Li, Yunqing, et al.
Published: (2024)

Revisiting the Message Passing in Heterophilous Graph Neural Networks
by: Zheng, Zhuonan, et al.
Published: (2024)

The Expressive Power of Graph Neural Networks: A Survey
by: Zhang, Bingxu, et al.
Published: (2023)

A Survey of Graph Neural Networks for Social Recommender Systems
by: Sharma, Kartik, et al.
Published: (2022)

GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks
by: Lu, Hsiao-Ying, et al.
Published: (2024)

FairACE: Achieving Degree Fairness in Graph Neural Networks via Contrastive and Adversarial Group-Balanced Training
by: Liu, Jiaxin, et al.
Published: (2025)

Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs
by: Heeg, Franziska, et al.
Published: (2023)

Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
by: Sato, Ryoma
Published: (2023)

Transferability of Graph Neural Networks using Graphon and Sampling Theories
by: Neuman, A. Martina, et al.
Published: (2023)

Interactive Event Sifting using Bayesian Graph Neural Networks
by: Nascimento, José, et al.
Published: (2024)

GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networks
by: Sirohi, Anuj Kumar, et al.
Published: (2024)

A Graph Neural Network deep-dive into successful counterattacks
by: Bekkers, Joris, et al.
Published: (2024)

PropEnc: A Property Encoder for Graph Neural Networks
by: Said, Anwar, et al.
Published: (2024)