Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) are a popular technique for modelling graph-structured data and computing node-level representations via aggregation of information from the neighborhood of each node.
Rules for ordering uncertain prospects
Josef Hadar and William R. Russell · 1969
Earlier work this paper cites.
Privacy-Preserving Graph Neural Network for Node Classification
Jun Zhou, Chaochao Chen, Longfei Zheng, Xiaolin Zheng, Bingzhe Wu, Ziqi Liu, and Li Wang · 2005
Earlier work this paper cites.
Calibrating Noise to Sensitivity in Private Data Analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
When Differential Privacy Meets Graph Neural Networks
Sina Sajadmanesh and Daniel Gatica-Perez · 2006
Earlier work this paper cites.
What Can We Learn Privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam D. Smith · 2008
Earlier work this paper cites.
Statistical Distributions
C. Forbes, M. Evans, N. Hastings, and B. Peacock · 2011
Earlier work this paper cites.
Private analysis of graph structure
Vishesh Karwa, Sofya Raskhodnikova, Adam Davison Smith, and Grigory Yaroslavtsev · 2011
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
Earlier work this paper cites.
Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Private Graphon Estimation for Sparse Graphs, 2015
Christian Borgs, Jennifer T. Chayes, and Adam Smith · 2015
Earlier work this paper cites.
Deep Learning with Differential Privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2016
Earlier work this paper cites.
Lipschitz Extensions for Node-Private Graph Statistics and the Generalized Exponential Mechanism
Sofya Raskhodnikova and Adam Smith · 2016
Cited alongside, same era.
Neural Message Passing for Quantum Chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
Cited alongside, same era.
Inductive Representation Learning on Large Graphs
William L. Hamilton, Rex Ying, and Jure Leskovec · 2017
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov · 2017
Cited alongside, same era.
Revealing Network Structure, Confidentially: Improved Rates for Node-Private Graphon Estimation, 2018
Christian Borgs, Jennifer Chayes, Adam Smith, and Ilias Zadik · 2018
Cited alongside, same era.
Graph neural networks for social recommendation
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin · 2019
Later among the works it cites.
Using attribution to decode binding mechanism in neural network models for chemistry
Kevin McCloskey, Ankur Taly, Federico Monti, Michael P Brenner, and Lucy J Colwell · 2019
Later among the works it cites.
Dynamic Graph CNN for Learning on Point Clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2019
Later among the works it cites.
Graph convolutional networks for text classification
Liang Yao, Chengsheng Mao, and Yuan Luo · 2019
Later among the works it cites.
Rich-Item Recommendations for Rich-Users: Exploiting Dynamic and Static Side Information, 2020
Amar Budhiraja, Gaurush Hiranandani, Darshak Chhatbar, Aditya Sinha, Navya Yarrabelly, Ayush Choure, Oluwasanmi Koyejo, and Prateek Jain · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Privacy Amplification by Iteration
Vitaly Feldman, Ilya Mironov, Kunal Talwar, and Abhradeep Thakurta · 2018
Cited alongside, same era.
Differentially Private Fair Learning
Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, and Jonathan R. Ullman · 2018
Cited alongside, same era.
Metric learning with spectral graph convolutions on brain connectivity networks
Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante, Martin Rajchl, Matthew Lee, Ben Glocker, and Daniel Rueckert · 2018
Cited alongside, same era.
A general approach to adding differential privacy to iterative training procedures, 2018
H. Brendan McMahan, Galen Andrew, Ulfar Erlingsson, Steve Chien, Ilya Mironov, Nicolas Papernot, and Peter Kairouz · 2018
Cited alongside, same era.
Graph Attention Networks, 2018
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Cited alongside, same era.
How Powerful are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
Cited alongside, same era.
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
Later among the works it cites.
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, and Lars Petersson · 2021
Closest in time.
Large-Scale Differentially Private BERT
Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, and Pasin Manurangsi · 2021
Closest in time.
Decision Making with Differential Privacy under a Fairness Lens
Ferdinando Fioretto, Cuong Tran, and Pascal Van Hentenryck · 2021
Closest in time.
Learning with User-Level Privacy
Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, and Ananda Theertha Suresh · 2021
Closest in time.
Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learning
Chuanqiang Shan, Huiyun Jiao, and Jie Fu · 2021
Closest in time.
Removing disparate impact on model accuracy in differentially private stochastic gradient descent
Depeng Xu, Wei Du, and Xintao Wu · 2021
Closest in time.