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Graph unlearning, which involves deleting graph elements such as nodes, node labels, and relationships from a trained graph neural network (GNN) model, is crucial for real-world applications where data elements may become irrelevant, inaccurate, or privacy-sensitive.
Investigating causal relations by econometric models and cross-spectral methods
Clive WJ Granger · 1969
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Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2019
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Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
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Ayush K Tarun, Vikram S Chundawat, Murari Mandal, and Mohan Kankanhalli · 2021
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Do transformers really perform badly for graph representation?
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Recommendation unlearning
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Mixed-privacy forgetting in deep networks
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Approximate data deletion from machine learning models
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou · 2021
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Certified graph unlearning
Eli Chien, Chao Pan, and Olgica Milenkovic · 2022
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Zero-shot machine unlearning
Vikram S Chundawat, Ayush K Tarun, Murari Mandal, and Mohan Kankanhalli · 2022
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Knowledge removal in sampling-based bayesian inference
Shaopeng Fu, Fengxiang He, and Dacheng Tao · 2022
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Rahif Kassab and Osvaldo Simeone · 2022
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Learn to forget: Machine unlearning via neuron masking
Zhuo Ma, Yang Liu, Ximeng Liu, Jian Liu, Jianfeng Ma, and Kui Ren · 2022
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Fast model editing at scale
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Recipe for a general, powerful, scalable graph transformer
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David M. Sommer, Liwei SOng, Sameer Wagh, and Prateek Mittal · 2022
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Federated unlearning via class-discriminative pruning
Junxiao Wang, Song Guo, Xin Xie, and Heng Qi · 2022
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Geodiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
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Grapheditor: An efficient graph representation learning and unlearning approach
Weilin Cong and Mehrdad Mahdavi · 2023
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Alexander Warnecke, Lukas Pirch, Christian Wressnegger, and Konrad Rieck · 2023
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