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Machine unlearning is a process of removing the impact of some training data from the machine learning (ML) models upon receiving removal requests.
Normalized Cuts and Image Segmentation
J. Shi and J. Malik · 1997
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A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
G. Karypis and V. Kumar · 1998
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Spectral Partitioning of Random Graphs
F. McSherry · 2001
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Community Structure in Social and Biological Networks
M. Girvan and M. E. J. Newman · 2002
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An Efficient k-Means Clustering Algorithm: Analysis and Implementation
T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu · 2002
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Local Graph Partitioning using PageRank Vectors
R. Andersen, F. R. K. Chung, and K. J. Lang · 2006
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Modularity and Community Structure in Networks
M. E. Newman · 2006
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Near Linear Time Algorithm to Detect Community Structures in Large-scale Networks
U. N. Raghavan, R. Albert, and S. Kumara · 2007
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Maps of Random Walks on Complex Networks Reveal Community Structure
M. Rosvall and C. T. Bergstrom · 2008
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Visualizing Data using t-SNE
L. van der Maaten and G. Hinton · 2008
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Label Propagation through Linear Neighborhoods
F. Wang and C. Zhang · 2008
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The Graph Neural Network Model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
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Performance of Modularity Maximization in Practical Contexts
B. H. Good, Y.-A. de Montjoye, and A. Clauset · 2010
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Graph Partitioning with Natural Cuts
D. Delling, A. V. Goldberg, I. P. Razenshteyn, and R. F. F. Werneck · 2011
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Balanced Label Propagation for Partitioning Massive Graphs
J. Ugander and L. Backstrom · 2013
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The Algorithmic Foundations of Differential Privacy
C. Dwork and A. Roth · 2014
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Balanced K-Means for Clustering
M. I. Malinen and P. Fr"anti · 2014
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Towards Making Systems Forget with Machine Unlearning
Y. Cao and J. Yang · 2015
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Multiway Spectral Community Detection in Networks
X. Zhang and M. E. J. Newman · 2015
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https://gdpr-info.eu/ , 2016
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node2vec: Scalable Feature Learning for Networks
A. Grover and J. Leskovec · 2016
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A Parallel Hill-Climbing Refinement Algorithm for Graph Partitioning
D. LaSalle and G. Karypis · 2016
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Revisiting Semi-Supervised Learning with Graph Embeddings
Z. Yang, W. W. Cohen, and R. Salakhutdinov · 2016
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Inductive Representation Learning on Large Graphs
W. L. Hamilton, Z. Ying, and J. Leskovec · 2017
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Semi-Supervised Classification with Graph Convolutional Networks
T. N. Kipf and M. Welling · 2017
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Balanced Clustering with Least Square Regression
H. Liu, J. Han, F. Nie, and X. Li · 2017
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Column Networks for Collective Classification
T. Pham, T. Tran, D. Q. Phung, and S. Venkatesh · 2017
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Membership Inference Attacks Against Machine Learning Models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
J. Wu, J. He, and J. Xu · 2019
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How Powerful are Graph Neural Networks
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
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Prioritized Restreaming Algorithms for Balanced Graph Partitioning
A. Awadelkarim and J. Ugander · 2020
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Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks
A. Golatkar, A. Achille, and S. Soatto · 2020
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Certified Data Removal from Machine Learning Models
C. Guo, T. Goldstein, A. Y. Hannun, and L. van der Maaten · 2020
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RoadTagger: Robust Road Attribute Inference with Graph Neural Networks
S. He, F. Bastani, S. Jagwani, E. Park, S. Abbar, M. Alizadeh, H. Balakrishnan, S. Chawla, S. Madden, and M. A. Sadeghi · 2020
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Cross View Link Prediction by Learning Noise-resilient Representation Consensus
X. Wei, L. Xu, B. Cao, and P. S. Yu · 2017
Cited alongside, same era.
Weisfeiler-Lehman Neural Machine for Link Prediction
M. Zhang and Y. Chen · 2017
Cited alongside, same era.
https://oag.ca.gov/privacy/ccpa , 2018
2018
Cited alongside, same era.
Efficient Repair of Polluted Machine Learning Systems via Causal Unlearning
Y. Cao, A. F. Yu, A. Aday, E. Stahl, J. Merwine, and J. Yang · 2018
Cited alongside, same era.
https://iapp.org/media/pdf/resource_center/Brazilian_General_Data_Protection_Law.pdf , 2018
2018
Cited alongside, same era.
DeepInf: Social Influence Prediction with Deep Learning
J. Qiu, J. Tang, H. Ma, Y. Dong, K. Wang, and J. Tang · 2018
Cited alongside, same era.
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PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest
A. Pal, C. Eksombatchai, Y. Zhou, B. Zhao, C. Rosenberg, and J. Leskovec · 2020
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Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification
Y. Shi, Z. Huang, S. Feng, H. Zhong, W. Wang, and Y. Sun · 2020
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Unifying Graph Convolutional Neural Networks and Label Propagation
H. Wang and J. Leskovec · 2020
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DeltaGrad: Rapid retraining of machine learning models
Y. Wu, E. Dobriban, and S. B. Davidson · 2020
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GraphSAINT: Graph Sampling Based Inductive Learning Method
H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. K. Prasanna · 2020
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Machine Unlearning
L. Bourtoule, V. Chandrasekaran, C. A. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot · 2021
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Machine Unlearning for Random Forests
J. Brophy and D. Lowd · 2021
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When Machine Unlearning Jeopardizes Privacy
M. Chen, Z. Zhang, T. Wang, M. Backes, M. Humbert, and Y. Zhang · 2021
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AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy
L. Du, Z. Zhang, S. Bai, C. Liu, S. Ji, P. Cheng, and J. Chen · 2021
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Adaptive Machine Unlearning
V. Gupta, C. Jung, S. Neel, A. Roth, S. Sharifi-Malvajerdi, and C. Waites · 2021
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Towards Efficient Motif-based Graph Partitioning: An Adaptive Sampling Approach
S. Huang, Y. Li, Z. Bao, and Z. Li · 2021
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Approximate Data Deletion from Machine Learning Models
Z. Izzo, M. A. Smart, K. Chaudhuri, and J. Zou · 2021
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Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
S. Neel, A. Roth, and S. Sharifi-Malvajerdi · 2021
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Continuous Release of Data Streams under both Centralized and Local Differential Privacy
T. Wang, J. Q. Chen, Z. Zhang, D. Su, Y. Cheng, Z. Li, N. Li, and S. Jha · 2021
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PrivSyn: Differentially Private Data Synthesis
Z. Zhang, T. Wang, N. Li, J. Honorio, M. Backes, S. He, J. Chen, and Y. Zhang · 2021
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Finding MNEMON: Reviving Memories of Node Embeddings
Y. Shen, Y. Han, Z. Zhang, M. Chen, T. Yu, M. Backes, Y. Zhang, and G. Stringhini · 2022
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