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Graph condensation (GC), which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has benefited various graph learning tasks.
Privacy against statistical inference
du Pin Calmon, F.; and Fawaz, N. 2012 · 2012
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Lee, D.-H.; et al. 2013 · 2013
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(Leveled) Fully Homomorphic Encryption without Bootstrapping
Brakerski, Z.; Gentry, C.; and Vaikuntanathan, V. 2014 · 2014
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Auto-Encoding Variational Bayes
Kingma, D. P.; and Welling, M. 2014 · 2014
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From the Information Bottleneck to the Privacy Funnel
Makhdoumi, A.; Salamatian, S.; Fawaz, N.; and Médard, M. 2014 · 2014
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Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Defferrard, M.; Bresson, X.; and Vandergheynst, P. 2016 · 2016
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Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N.; and Welling, M. 2016 · 2016
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Learning Without Forgetting
Li, Z.; and Hoiem, D. 2016 · 2016
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Deep Variational Information Bottleneck
Alemi, A. A.; Fischer, I.; Dillon, J. V.; and Murphy, K. 2017 · 2017
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Protein Interface Prediction using Graph Convolutional Networks
Fout, A.; Byrd, J.; Shariat, B.; and Ben-Hur, A. 2017 · 2017
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Inductive Representation Learning on Large Graphs
Hamilton, W. L.; Ying, Z.; and Leskovec, J. 2017 · 2017
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Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N.; and Welling, M. 2017 · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
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Membership Inference Attacks Against Machine Learning Models
Shokri, R.; Stronati, M.; Song, C.; and Shmatikov, V. 2017 · 2017
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The eu general data protection regulation (gdpr)
Voigt, P.; and Von dem Bussche, A. 2017 · 2017
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Guaranteeing Local Differential Privacy on Ultra-Low-Power Systems
Choi, W.; Tomei, M.; Vicarte, J. R. S.; Hanumolu, P. K.; and Kumar, R. 2018 · 2018
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Query-Efficient Black-Box Attack by Active Learning
Li, P.; Yi, J.; and Zhang, L. 2018 · 2018
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Ray: A Distributed Framework for Emerging AI Applications
Moritz, P.; Nishihara, R.; Wang, S.; Tumanov, A.; Liaw, R.; Liang, E.; Elibol, M.; Yang, Z.; Paul, W.; Jordan, M. I.; and Stoica, I. 2018 · 2018
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Machine Learning with Membership Privacy using Adversarial Regularization
Nasr, M.; Shokri, R.; and Houmansadr, A. 2018 · 2018
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Graph Attention Networks
Velickovic, P.; Cucurull, G.; Casanova, A.; Romero, A.; Liò, P.; and Bengio, Y. 2018 · 2018
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Wang, T.; Zhu, J.; Torralba, A.; and Efros, A. A. 2018 · 2018
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The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
Carlini, N.; Liu, C.; Erlingsson, Ú.; Kos, J.; and Song, D. 2019 · 2019
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Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
Fan, S.; Zhu, J.; Han, X.; Shi, C.; Hu, L.; Ma, B.; and Li, Y. 2019 · 2019
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Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Klicpera, J.; Bojchevski, A.; and Günnemann, S. 2019 · 2019
Cited alongside, same era.
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
Nasr, M.; Shokri, R.; and Houmansadr, A. 2019 · 2019
Cited alongside, same era.
A Survey on Heterogeneous Federated Learning
Gao, D.; Yao, X.; and Yang, Q. 2022 · 2022
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Label-Only Model Inversion Attacks via Boundary Repulsion
Kahla, M.; Chen, S.; Just, H. A.; and Jia, R. 2022 · 2022
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Graph Condensation via Receptive Field Distribution Matching
Liu, M.; Li, S.; Chen, X.; and Song, L. 2022 · 2022
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BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling
Wan, C.; Li, Y.; Li, A.; Kim, N. S.; and Lin, Y. 2022 · 2022
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GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs
Wang, B.; Li, A.; Pang, M.; Li, H.; and Chen, Y. 2022 · 2022
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ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models
Salem, A.; Zhang, Y.; Humbert, M.; Berrang, P.; Fritz, M.; and Backes, M. 2019 · 2019
Cited alongside, same era.
Simplifying Graph Convolutional Networks
Wu, F.; Jr., A. H. S.; Zhang, T.; Fifty, C.; Yu, T.; and Weinberger, K. Q. 2019 · 2019
Cited alongside, same era.
How Powerful are Graph Neural Networks?
Xu, K.; Hu, W.; Leskovec, J.; and Jegelka, S. 2019 · 2019
Cited alongside, same era.
Graph Information Bottleneck
Wu, T.; Ren, H.; Li, P.; and Leskovec, J. 2020 · 2020
Cited alongside, same era.
Federated Learning - Privacy and Incentive , volume 12500 of Lecture Notes in Computer Science
Yang, Q.; Fan, L.; and Yu, H., eds. 2020 · 2020
Cited alongside, same era.
Beyond Low-frequency Information in Graph Convolutional Networks
Bo, D.; Wang, X.; Shi, C.; and Shen, H. 2021 · 2021
Cited alongside, same era.
Node-Level Membership Inference Attacks Against Graph Neural Networks
He, X.; Wen, R.; Wu, Y.; Backes, M.; Shen, Y.; and Zhang, Y. 2021 · 2021
Cited alongside, same era.
A federated graph neural network framework for privacy-preserving personalization
Wu, C.; Wu, F.; Lyu, L.; Qi, T.; Huang, Y.; and Xie, X. 2022 · 2022
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BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule
Zhang, M.; Pan, S.; Chang, X.; Su, S.; Hu, J.; Haffari, G.; and Yang, B. 2022 · 2022
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Federated learning of molecular properties with graph neural networks in a heterogeneous setting
Zhu, W.; Luo, J.; and White, A. D. 2022 · 2022
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Personalized Subgraph Federated Learning
Baek, J.; Jeong, W.; Jin, J.; Yoon, J.; and Hwang, S. J. 2023 · 2023
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Specformer: Spectral Graph Neural Networks Meet Transformers
Bo, D.; Shi, C.; Wang, L.; and Liao, R. 2023 · 2023
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Poster: Membership Inference Attacks via Contrastive Learning
Chen, D.; Liu, X.; Cui, J.; and Zhong, H. 2023 · 2023
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A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy
Dai, E.; Cui, L.; Wang, Z.; Tang, X.; Wang, Y.; Cheng, M. X.; Yin, B.; and Wang, S. 2023 · 2023
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Vulnerability Intelligence Alignment via Masked Graph Attention Networks
Qin, Y.; Xiao, Y.; and Liao, X. 2023 · 2023
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Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
Tan, Y.; Liu, Y.; Long, G.; Jiang, J.; Lu, Q.; and Zhang, C. 2023 · 2023
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FedFed: Feature Distillation against Data Heterogeneity in Federated Learning
Yang, Z.; Zhang, Y.; Zheng, Y.; Tian, X.; Peng, H.; Liu, T.; and Han, B. 2023 · 2023
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FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
Yao, Y.; Jin, W.; Ravi, S.; and Joe-Wong, C. 2023 · 2023
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Dataset Condensation with Distribution Matching
Zhao, B.; and Bilen, H. 2023 · 2023
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Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
Zheng, X.; Zhang, M.; Chen, C.; Nguyen, Q. V. H.; Zhu, X.; and Pan, S. 2023 · 2023
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Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?
Zhang, Z.; Wang, X.; Zhou, H.; Yu, Y.; Zhang, M.; Yang, C.; and Shi, C. 2024 · 2024
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