Saved in:
| Main Authors: | Shen, Bolin, Seraj, Md Shamim, Cheng, Zhan, Chakraborty, Shayok, Dong, Yushun |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.20418 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?
by: Zhao, Kaixiang, et al.
Published: (2026)
by: Zhao, Kaixiang, et al.
Published: (2026)
CREDIT: Certified Ownership Verification of Deep Neural Networks Against Model Extraction Attacks
by: Shen, Bolin, et al.
Published: (2026)
by: Shen, Bolin, et al.
Published: (2026)
ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks
by: Cheng, Zhan, et al.
Published: (2025)
by: Cheng, Zhan, et al.
Published: (2025)
An Analysis of Active Learning Algorithms using Real-World Crowd-sourced Text Annotations
by: Totakura, Varun, et al.
Published: (2026)
by: Totakura, Varun, et al.
Published: (2026)
CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition
by: Wang, Zebin, et al.
Published: (2025)
by: Wang, Zebin, et al.
Published: (2025)
MetaErr: Towards Predicting Error Patterns in Deep Neural Networks
by: Totakura, Varun, et al.
Published: (2026)
by: Totakura, Varun, et al.
Published: (2026)
A Survey on Model Extraction Attacks and Defenses for Large Language Models
by: Zhao, Kaixiang, et al.
Published: (2025)
by: Zhao, Kaixiang, et al.
Published: (2025)
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments
by: Zhao, Kaixiang, et al.
Published: (2025)
by: Zhao, Kaixiang, et al.
Published: (2025)
A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives
by: Zhao, Kaixiang, et al.
Published: (2025)
by: Zhao, Kaixiang, et al.
Published: (2025)
From Static Constraints to Dynamic Adaptation: Sample-Level Constraint Relaxation for Offline-to-Online Reinforcement Learning
by: Zu, Lipeng, et al.
Published: (2025)
by: Zu, Lipeng, et al.
Published: (2025)
Certified Defense on the Fairness of Graph Neural Networks
by: Dong, Yushun, et al.
Published: (2023)
by: Dong, Yushun, et al.
Published: (2023)
FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification
by: Jiang, Chutian, et al.
Published: (2024)
by: Jiang, Chutian, et al.
Published: (2024)
From Rubrics to Reliable Scores: Evidence-Grounded Text Evaluation with LLM Judges
by: Hong, Yihan, et al.
Published: (2026)
by: Hong, Yihan, et al.
Published: (2026)
Towards Certified Unlearning for Deep Neural Networks
by: Zhang, Binchi, et al.
Published: (2024)
by: Zhang, Binchi, et al.
Published: (2024)
Towards In-Vehicle Multi-Task Facial Attribute Recognition: Investigating Synthetic Data and Vision Foundation Models
by: Seraj, Esmaeil, et al.
Published: (2024)
by: Seraj, Esmaeil, et al.
Published: (2024)
Towards Quantifying the Hessian Structure of Neural Networks
by: Dong, Zhaorui, et al.
Published: (2025)
by: Dong, Zhaorui, et al.
Published: (2025)
Locality-Aware Graph-Rewiring in GNNs
by: Barbero, Federico, et al.
Published: (2023)
by: Barbero, Federico, et al.
Published: (2023)
Towards Precise Prediction Uncertainty in GNNs: Refining GNNs with Topology-grouping Strategy
by: Seo, Hyunjin, et al.
Published: (2024)
by: Seo, Hyunjin, et al.
Published: (2024)
SLA-MORL: SLA-Aware Multi-Objective Reinforcement Learning for HPC Resource Optimization
by: Mostafa, Seraj Al Mahmud, et al.
Published: (2025)
by: Mostafa, Seraj Al Mahmud, et al.
Published: (2025)
A Systematic Study of Model Extraction Attacks on Graph Foundation Models
by: Xu, Haoyan, et al.
Published: (2025)
by: Xu, Haoyan, et al.
Published: (2025)
CAPoW: Context-Aware AI-Assisted Proof of Work based DDoS Defense
by: Chakraborty, Trisha, et al.
Published: (2023)
by: Chakraborty, Trisha, et al.
Published: (2023)
TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series
by: Piao, Xihao, et al.
Published: (2026)
by: Piao, Xihao, et al.
Published: (2026)
What Do GNNs Actually Learn? Towards Understanding their Representations
by: Nikolentzos, Giannis, et al.
Published: (2023)
by: Nikolentzos, Giannis, et al.
Published: (2023)
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
by: Hussain, Md Shamim, et al.
Published: (2024)
by: Hussain, Md Shamim, et al.
Published: (2024)
A Signed Graph Approach to Understanding and Mitigating Oversmoothing in GNNs
by: Wang, Jiaqi, et al.
Published: (2025)
by: Wang, Jiaqi, et al.
Published: (2025)
CTS-Bench: Benchmarking Graph Coarsening Trade-offs for GNNs in Clock Tree Synthesis
by: Khadka, Barsat, et al.
Published: (2026)
by: Khadka, Barsat, et al.
Published: (2026)
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
by: Luo, Yuankai, et al.
Published: (2024)
by: Luo, Yuankai, et al.
Published: (2024)
Multiplicative Logit Adjustment Approximates Neural-Collapse-Aware Decision Boundary Adjustment
by: Hasegawa, Naoya, et al.
Published: (2024)
by: Hasegawa, Naoya, et al.
Published: (2024)
Message Passing on the Edge: Towards Scalable and Expressive GNNs
by: Barceló, Pablo, et al.
Published: (2025)
by: Barceló, Pablo, et al.
Published: (2025)
GraphBridge: Towards Arbitrary Transfer Learning in GNNs
by: Ju, Li, et al.
Published: (2025)
by: Ju, Li, et al.
Published: (2025)
Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?
by: Nimase, Ojas, et al.
Published: (2026)
by: Nimase, Ojas, et al.
Published: (2026)
You Only Need Your Transformer 25% of the Time: Meaning-First Execution for Eliminating Unnecessary Inference
by: Shamim, Ryan
Published: (2025)
by: Shamim, Ryan
Published: (2025)
A Benchmark for Fairness-Aware Graph Learning
by: Dong, Yushun, et al.
Published: (2024)
by: Dong, Yushun, et al.
Published: (2024)
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs
by: Chen, Zihan, et al.
Published: (2025)
by: Chen, Zihan, et al.
Published: (2025)
Efficient Model Extraction via Boundary Sampling
by: Dor, Maor Biton, et al.
Published: (2024)
by: Dor, Maor Biton, et al.
Published: (2024)
On Efficient Scaling of GNNs via IO-Aware Layers Implementations
by: Fomina, Daria, et al.
Published: (2026)
by: Fomina, Daria, et al.
Published: (2026)
Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm
by: Liu, Meng, et al.
Published: (2022)
by: Liu, Meng, et al.
Published: (2022)
IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks
by: Dong, Yushun, et al.
Published: (2024)
by: Dong, Yushun, et al.
Published: (2024)
Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs
by: Frasca, Fabrizio, et al.
Published: (2024)
by: Frasca, Fabrizio, et al.
Published: (2024)
ADAGE: Active Defenses Against GNN Extraction
by: Xu, Jing, et al.
Published: (2025)
by: Xu, Jing, et al.
Published: (2025)
Similar Items
-
GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?
by: Zhao, Kaixiang, et al.
Published: (2026) -
CREDIT: Certified Ownership Verification of Deep Neural Networks Against Model Extraction Attacks
by: Shen, Bolin, et al.
Published: (2026) -
ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks
by: Cheng, Zhan, et al.
Published: (2025) -
An Analysis of Active Learning Algorithms using Real-World Crowd-sourced Text Annotations
by: Totakura, Varun, et al.
Published: (2026) -
CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition
by: Wang, Zebin, et al.
Published: (2025)