Prompt-based Unifying Inference Attack on Graph Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Wei, Yuecen, Fu, Xingcheng, Liu, Lingyun, Sun, Qingyun, Peng, Hao, Hu, Chunming |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding
by: Wei, Yuecen, et al.
Published: (2023)
by: Wei, Yuecen, et al.
Published: (2023)
An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks
by: Wang, Jinyan, et al.
Published: (2025)
by: Wang, Jinyan, et al.
Published: (2025)
Privacy Auditing of Multi-domain Graph Pre-trained Model under Membership Inference Attacks
by: Luo, Jiayi, et al.
Published: (2025)
by: Luo, Jiayi, et al.
Published: (2025)
Hyperbolic Geometric Latent Diffusion Model for Graph Generation
by: Fu, Xingcheng, et al.
Published: (2024)
by: Fu, Xingcheng, et al.
Published: (2024)
Mitigating Message Imbalance in Fraud Detection with Dual-View Graph Representation Learning
by: Song, Yudan, et al.
Published: (2025)
by: Song, Yudan, et al.
Published: (2025)
Toward a Unified Geometry Understanding: Riemannian Diffusion Framework for Graph Generation and Prediction
by: Gao, Yisen, et al.
Published: (2025)
by: Gao, Yisen, et al.
Published: (2025)
GC-Bench: An Open and Unified Benchmark for Graph Condensation
by: Sun, Qingyun, et al.
Published: (2024)
by: Sun, Qingyun, et al.
Published: (2024)
Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure Purification
by: Luo, Jiayi, et al.
Published: (2025)
by: Luo, Jiayi, et al.
Published: (2025)
SA$^{2}$GFM: Enhancing Robust Graph Foundation Models with Structure-Aware Semantic Augmentation
by: Shi, Junhua, et al.
Published: (2025)
by: Shi, Junhua, et al.
Published: (2025)
Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models
by: Yuan, Haonan, et al.
Published: (2026)
by: Yuan, Haonan, et al.
Published: (2026)
Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning
by: Huang, Yi, et al.
Published: (2026)
by: Huang, Yi, et al.
Published: (2026)
Evolving Graph Learning for Out-of-Distribution Generalization in Non-stationary Environments
by: Sun, Qingyun, et al.
Published: (2025)
by: Sun, Qingyun, et al.
Published: (2025)
Dynamic Graph Information Bottleneck
by: Yuan, Haonan, et al.
Published: (2024)
by: Yuan, Haonan, et al.
Published: (2024)
Zero-shot Generalizable Graph Anomaly Detection with Mixture of Riemannian Experts
by: Zhao, Xinyu, et al.
Published: (2026)
by: Zhao, Xinyu, et al.
Published: (2026)
RAG-GFM: Overcoming In-Memory Bottlenecks in Graph Foundation Models via Retrieval-Augmented Generation
by: Yuan, Haonan, et al.
Published: (2026)
by: Yuan, Haonan, et al.
Published: (2026)
Graph Size-imbalanced Learning with Energy-guided Structural Smoothing
by: Qin, Jiawen, et al.
Published: (2024)
by: Qin, Jiawen, et al.
Published: (2024)
GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning
by: Yuan, Haonan, et al.
Published: (2025)
by: Yuan, Haonan, et al.
Published: (2025)
Discrete Curvature Graph Information Bottleneck
by: Fu, Xingcheng, et al.
Published: (2024)
by: Fu, Xingcheng, et al.
Published: (2024)
Safeguarding Graph Neural Networks against Topology Inference Attacks
by: Fu, Jie, et al.
Published: (2025)
by: Fu, Jie, et al.
Published: (2025)
GraphKeeper: Graph Domain-Incremental Learning via Knowledge Disentanglement and Preservation
by: Guo, Zihao, et al.
Published: (2025)
by: Guo, Zihao, et al.
Published: (2025)
A Unified Graph Selective Prompt Learning for Graph Neural Networks
by: Jiang, Bo, et al.
Published: (2024)
by: Jiang, Bo, et al.
Published: (2024)
Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck
by: Fu, Xingcheng, et al.
Published: (2024)
by: Fu, Xingcheng, et al.
Published: (2024)
Graph Neural Networks Automated Design and Deployment on Device-Edge Co-Inference Systems
by: Zhou, Ao, et al.
Published: (2024)
by: Zhou, Ao, et al.
Published: (2024)
GraphMoRE: Mitigating Topological Heterogeneity via Mixture of Riemannian Experts
by: Guo, Zihao, et al.
Published: (2024)
by: Guo, Zihao, et al.
Published: (2024)
DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models
by: Yuan, Haonan, et al.
Published: (2024)
by: Yuan, Haonan, et al.
Published: (2024)
Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks
by: Lin, Minhua, et al.
Published: (2024)
by: Lin, Minhua, et al.
Published: (2024)
Is the Information Bottleneck Robust Enough? Towards Label-Noise Resistant Information Bottleneck Learning
by: Huang, Yi, et al.
Published: (2025)
by: Huang, Yi, et al.
Published: (2025)
Robust Graph Condensation via Classification Complexity Mitigation
by: Luo, Jiayi, et al.
Published: (2025)
by: Luo, Jiayi, et al.
Published: (2025)
IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning
by: Qin, Jiawen, et al.
Published: (2024)
by: Qin, Jiawen, et al.
Published: (2024)
Edge Prompt Tuning for Graph Neural Networks
by: Fu, Xingbo, et al.
Published: (2025)
by: Fu, Xingbo, et al.
Published: (2025)
Galaxy Walker: Geometry-aware VLMs For Galaxy-scale Understanding
by: Chen, Tianyu, et al.
Published: (2025)
by: Chen, Tianyu, et al.
Published: (2025)
GraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks
by: Fu, Xingbo, et al.
Published: (2025)
by: Fu, Xingbo, et al.
Published: (2025)
Spiking Graph Neural Network on Riemannian Manifolds
by: Sun, Li, et al.
Published: (2024)
by: Sun, Li, et al.
Published: (2024)
Graph-Level Label-Only Membership Inference Attack against Graph Neural Networks
by: Dai, Jiazhu, et al.
Published: (2025)
by: Dai, Jiazhu, et al.
Published: (2025)
Adaptive Dual Prompting: Hierarchical Debiasing for Fairness-aware Graph Neural Networks
by: Yang, Yuhan, et al.
Published: (2025)
by: Yang, Yuhan, et al.
Published: (2025)
Leveraging Personalized PageRank and Higher-Order Topological Structures for Heterophily Mitigation in Graph Neural Networks
by: Wang, Yumeng, et al.
Published: (2025)
by: Wang, Yumeng, et al.
Published: (2025)
Unsupervised Prompting for Graph Neural Networks
by: Baghershahi, Peyman, et al.
Published: (2025)
by: Baghershahi, Peyman, et al.
Published: (2025)
Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networks
by: Li, Kunhao, et al.
Published: (2025)
by: Li, Kunhao, et al.
Published: (2025)
LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering
by: Sun, Li, et al.
Published: (2024)
by: Sun, Li, et al.
Published: (2024)
Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction
by: Ye, Zesheng, et al.
Published: (2025)
by: Ye, Zesheng, et al.
Published: (2025)
Similar Items
-
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding
by: Wei, Yuecen, et al.
Published: (2023) -
An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks
by: Wang, Jinyan, et al.
Published: (2025) -
Privacy Auditing of Multi-domain Graph Pre-trained Model under Membership Inference Attacks
by: Luo, Jiayi, et al.
Published: (2025) -
Hyperbolic Geometric Latent Diffusion Model for Graph Generation
by: Fu, Xingcheng, et al.
Published: (2024) -
Mitigating Message Imbalance in Fraud Detection with Dual-View Graph Representation Learning
by: Song, Yudan, et al.
Published: (2025)