Graph Unlearning

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11 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

When Forgotten Facts Return: Relearning-Aware Unlearning of Knowledge Graph Embeddings via Tangent Kernels
Oct 22, 2026Parnian Fazel, Mohammad M Maheri, Hamed HaddadiMachine Unlearning Graph Unlearning
Imperial College London · University of Oxford
Lethe: Link Inference Attacks For Evaluation of Edge Unlearning Methods
Oct 15, 2026Andrea D'Angelo, Francesco Gullo, Davide Mottin +2Graph Unlearning Machine Unlearning
University of Aarhus Aarhus, Denmark · University of L’Aquila L’Aquila, Italy · Indian Institute of Technology Delhi Delhi, India +1
CUNO: Curriculum and Preference Optimization for Stable Graph Unlearning under Mass Deletion
Sep 8, 2026Chenhan Zhang, Ali Braytee, Madhushi Bandara +4Graph Unlearning Preference Optimization
University of Technology Sydney
Unlearning on Spatio-Temporal Graphs through Subgraph Virtual Edge Reconstruction
Aug 29, 2026Qiming Guo, Wenbo Sun, Chen Pan +2Graph Unlearning Machine Unlearning
Texas A&M University - Corpus Christi Corpus Christi, Texas, USA · Delft University of Technology Delft, Netherlands · University of Texas at San Antonio San Antonio, Texas, USA +1
Spatial Entropy based Partitioning for Spatiotemporal Graph Unlearning
Aug 29, 2026Qiming Guo, Wenbo Sun, Ye Wang +1Graph Partitioning Graph Unlearning
Texas A&M University - Corpus Christi, Corpus Christi, TX, USA · Delft University of Technology, Netherlands · Biogen, Cambridge, MA, USA
MMFGU: Multimodal Federated Graph Unlearning
Jul 30, 2026Haodong Lu, Zekai Chen, Weiwei Ji +5Multimodal Federated Learning Federated Unlearning
Beijing Institute of Technology Beijing, China
GDGU: A Gradient Difference-based Graph Unlearning Method for Cyberattack Localization in Electric Vehicle Charging Networks
Jun 17, 2026Nanhong Liu, Mucun Sun, Jie ZhangGraph Neural Networks Graph Unlearning
Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA · Idaho National Laboratory, Idaho Falls, ID 83415, USA · Department of Mechanical Engineering and (Affilated) Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA
Closed-Form Node Classification with Exact Graph Unlearning
May 25, 2026Aditya Gaur, Charu SharmaGraph Representation Learning Privacy-Preserving ML
Machine Learning Lab · IIIT Hyderabad
Stable Multimodal Graph Unlearning via Feature-Dimension Aware Quantile Selection
May 5, 2026Jingjing Zhou, Yongshuai Yang, Qing Qing +5Graph Unlearning Privacy-Preserving ML
School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou, China · College of Computer Science and Technology, Jilin University, Changchun 130012, China · School of Computing Technologies, RMIT University, Melbourne, VIC 3000, Australia +1
Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
Mar 19, 2026Jiahao Zhang, Yilong Wang, Suhang WangGraph Unlearning Adversarial Attacks
The Pennsylvania State University University Park, PA, USA
GraphToxin: Reconstructing Full Unlearned Graphs from Graph Unlearning
Nov 14, 2025Ying Song, Balaji PalanisamyGraph Neural Networks Graph Unlearning
University of Pittsburgh Pittsburgh, PA, USA

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When Forgotten Facts Return: Relearning-Aware Unlearning of Knowledge Graph Embeddings via Tangent Kernels

Parnian Fazel, Mohammad M Maheri, Hamed Haddadi

Imperial College London · University of Oxford

Machine Unlearning Graph Unlearning KG Embedding

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Approximate unlearning answers a deletion request on a knowledge-graph embedding by editing entity embeddings instead of retraining from scratch. These embeddings keep being updated as new facts arrive. Under this ordinary continued training, forgotten facts recover more than in the exact-unlearning baseline, a model retrained without them and given the same continued training. We trace this relearning to entity embeddings shared by forgotten and new facts. To our knowledge, this is the first study of relearning in knowledge-graph embeddings. Relearning-robust methods developed for deep networks do not account for the graph structure underlying this recovery. We propose Kernel-Anticipated Unlearning (KAU), which uses the tangent kernel to model how continued training changes forgotten facts' scores through these shared parameters. KAU expresses this first-order response in closed form and differentiates through it inside the forgetting loss. The objective steers edits toward directions that continued training reverses less. We compare KAU with state-of-the-art knowledge-graph unlearners and relearning-robust methods under identical continued training, measuring leakage beyond the exact-unlearning baseline. Across three backbones, KAU reduces the strongest knowledge-graph unlearner's leakage by 46-63% on FB15k-237 and 50-82% on CoDEx-L, at higher retained utility.