Adaptive Graph Unlearning
Fuente:
arXiv
Saved in:
| Main Authors: | Ding, Pengfei, Wang, Yan, Liu, Guanfeng, Zhu, Jiajie |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Re-understanding Graph Unlearning through Memorization
by: Ding, Pengfei, et al.
Published: (2026)
by: Ding, Pengfei, et al.
Published: (2026)
Few-shot Learning on Heterogeneous Graphs: Challenges, Progress, and Prospects
by: Ding, Pengfei, et al.
Published: (2024)
by: Ding, Pengfei, et al.
Published: (2024)
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
by: Ding, Pengfei, et al.
Published: (2024)
by: Ding, Pengfei, et al.
Published: (2024)
Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks
by: Zhang, Han, et al.
Published: (2025)
by: Zhang, Han, et al.
Published: (2025)
Graph self-supervised learning based on frequency corruption
by: Li, Haojie, et al.
Published: (2026)
by: Li, Haojie, et al.
Published: (2026)
Federated Graph Unlearning
by: Ai, Yuming, et al.
Published: (2025)
by: Ai, Yuming, et al.
Published: (2025)
Cross-Domain Fake News Detection on Unseen Domains via LLM-Based Domain-Aware User Modeling
by: Yang, Xuankai, et al.
Published: (2026)
by: Yang, Xuankai, et al.
Published: (2026)
Auditing Approximate Machine Unlearning for Differentially Private Models
by: Gu, Yuechun, et al.
Published: (2025)
by: Gu, Yuechun, et al.
Published: (2025)
How Do Graph Signals Affect Recommendation: Unveiling the Mystery of Low and High-Frequency Graph Signals
by: Liu, Feng, et al.
Published: (2025)
by: Liu, Feng, et al.
Published: (2025)
Streamlined Federated Unlearning: Unite as One to Be Highly Efficient
by: Zhou, Lei, et al.
Published: (2024)
by: Zhou, Lei, et al.
Published: (2024)
Graph Unlearning: Efficient Node Removal in Graph Neural Networks
by: Guan, Faqian, et al.
Published: (2025)
by: Guan, Faqian, et al.
Published: (2025)
Edge Unlearning is Not "on Edge"! An Adaptive Exact Unlearning System on Resource-Constrained Devices
by: Xia, Xiaoyu, et al.
Published: (2024)
by: Xia, Xiaoyu, et al.
Published: (2024)
Graph Federated Unlearning for Privacy Preservation
by: Ma, Ruotong, et al.
Published: (2026)
by: Ma, Ruotong, et al.
Published: (2026)
Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
by: Zhang, Jiahao, et al.
Published: (2026)
by: Zhang, Jiahao, et al.
Published: (2026)
Efficient Federated Unlearning with Adaptive Differential Privacy Preservation
by: Jiang, Yu, et al.
Published: (2024)
by: Jiang, Yu, et al.
Published: (2024)
Spatio-Temporal Graph Unlearning
by: Guo, Qiming, et al.
Published: (2025)
by: Guo, Qiming, et al.
Published: (2025)
Geometric Mixture-of-Experts with Curvature-Guided Adaptive Routing for Graph Representation Learning
by: Cao, Haifang, et al.
Published: (2026)
by: Cao, Haifang, et al.
Published: (2026)
A Survey of Graph Unlearning
by: Said, Anwar, et al.
Published: (2023)
by: Said, Anwar, et al.
Published: (2023)
On Large Language Model Continual Unlearning
by: Gao, Chongyang, et al.
Published: (2024)
by: Gao, Chongyang, et al.
Published: (2024)
Graph Unlearning with Efficient Partial Retraining
by: Zhang, Jiahao, et al.
Published: (2024)
by: Zhang, Jiahao, et al.
Published: (2024)
GraphToxin: Reconstructing Full Unlearned Graphs from Graph Unlearning
by: Song, Ying, et al.
Published: (2025)
by: Song, Ying, et al.
Published: (2025)
Unlearning Inversion Attacks for Graph Neural Networks
by: Zhang, Jiahao, et al.
Published: (2025)
by: Zhang, Jiahao, et al.
Published: (2025)
An Illusion of Unlearning? Assessing Machine Unlearning Through Internal Representations
by: Gao, Yichen, et al.
Published: (2026)
by: Gao, Yichen, et al.
Published: (2026)
Federated Knowledge Graph Unlearning via Diffusion Model
by: Liu, Bingchen, et al.
Published: (2024)
by: Liu, Bingchen, et al.
Published: (2024)
Certified Signed Graph Unlearning
by: Zhao, Junpeng, et al.
Published: (2025)
by: Zhao, Junpeng, et al.
Published: (2025)
TCGU: Data-centric Graph Unlearning based on Transferable Condensation
by: Li, Fan, et al.
Published: (2024)
by: Li, Fan, et al.
Published: (2024)
Reinforcement Unlearning
by: Ye, Dayong, et al.
Published: (2023)
by: Ye, Dayong, et al.
Published: (2023)
Enabling Group Fairness in Graph Unlearning via Bi-level Debiasing
by: Liu, Yezi, et al.
Published: (2025)
by: Liu, Yezi, et al.
Published: (2025)
Towards Effective Evaluations and Comparisons for LLM Unlearning Methods
by: Wang, Qizhou, et al.
Published: (2024)
by: Wang, Qizhou, et al.
Published: (2024)
Label Smoothing Improves Machine Unlearning
by: Di, Zonglin, et al.
Published: (2024)
by: Di, Zonglin, et al.
Published: (2024)
Efficient Knowledge Graph Unlearning with Zeroth-order Information
by: Xiao, Yang, et al.
Published: (2025)
by: Xiao, Yang, et al.
Published: (2025)
PolyG: Adaptive Graph Traversal for Diverse GraphRAG Questions
by: Liu, Renjie, et al.
Published: (2025)
by: Liu, Renjie, et al.
Published: (2025)
Community-Centric Graph Unlearning
by: Li, Yi, et al.
Published: (2024)
by: Li, Yi, et al.
Published: (2024)
Distill to Delete: Unlearning in Graph Networks with Knowledge Distillation
by: Sinha, Yash, et al.
Published: (2023)
by: Sinha, Yash, et al.
Published: (2023)
Closed-Form Node Classification with Exact Graph Unlearning
by: Gaur, Aditya, et al.
Published: (2026)
by: Gaur, Aditya, et al.
Published: (2026)
Towards Efficient Target-Level Machine Unlearning Based on Essential Graph
by: Xu, Heng, et al.
Published: (2024)
by: Xu, Heng, et al.
Published: (2024)
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning
by: Wu, Tao, et al.
Published: (2024)
by: Wu, Tao, et al.
Published: (2024)
Adaptive Domain Inference Attack with Concept Hierarchy
by: Gu, Yuechun, et al.
Published: (2023)
by: Gu, Yuechun, et al.
Published: (2023)
OpenGU: A Comprehensive Benchmark for Graph Unlearning
by: Fan, Bowen, et al.
Published: (2025)
by: Fan, Bowen, et al.
Published: (2025)
Scalable and Certifiable Graph Unlearning: Overcoming the Approximation Error Barrier
by: Yi, Lu, et al.
Published: (2024)
by: Yi, Lu, et al.
Published: (2024)
Similar Items
-
Re-understanding Graph Unlearning through Memorization
by: Ding, Pengfei, et al.
Published: (2026) -
Few-shot Learning on Heterogeneous Graphs: Challenges, Progress, and Prospects
by: Ding, Pengfei, et al.
Published: (2024) -
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
by: Ding, Pengfei, et al.
Published: (2024) -
Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks
by: Zhang, Han, et al.
Published: (2025) -
Graph self-supervised learning based on frequency corruption
by: Li, Haojie, et al.
Published: (2026)