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Large-scale graphs are valuable for graph representation learning, yet the abundant data in these graphs hinders the efficiency of the training process.
A kernel two-sample test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. J. Smola · 2012
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Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 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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Active learning for convolutional neural networks: A core-set approach
O. Sener and S. Savarese · 2018
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Graph attention networks
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
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T. Wang, J.-Y. Zhu, A. Torralba, and A. A. Efros · 2018
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Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
S. S. Du, K. Hou, R. Salakhutdinov, B. Póczos, R. Wang, and K. Xu · 2019
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Predict then propagate: Graph neural networks meet personalized PageRank
J. Klicpera, A. Bojchevski, and S. Günnemann · 2019
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Simplifying graph convolutional networks
F. Wu, A. H. S. Jr., T. Zhang, C. Fifty, T. Yu, and K. Q. Weinberger · 2019
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COPT: Coordinated optimal transport on graphs
Y. Dong and W. Sawin · 2020
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Open graph benchmark: Datasets for machine learning on graphs
W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec · 2020
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Design space for graph neural networks
J. You, Z. Ying, and J. Leskovec · 2020
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Dataset condensation with differentiable siamese augmentation
B. Zhao and H. Bilen · 2021
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Dataset condensation with gradient matching
B. Zhao, K. R. Mopuri, and H. Bilen · 2021
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Dataset Distillation by Matching Training Trajectories
G. Cazenavette, T. Wang, A. Torralba, A. A. Efros, and J.-Y. Zhu · 2022
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DC-BENCH: Dataset condensation benchmark
J. Cui, R. Wang, S. Si, and C.-J. Hsieh · 2022
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Faster Hyperparameter Search on Graphs via Calibrated Dataset Condensation
M. Ding, X. Liu, T. Rabbani, and F. Huang · 2022
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Graph Condensation for Inductive Node Representation Learning
X. Gao, T. Chen, Y. Zang, W. Zhang, Q. V. H. Nguyen, K. Zheng, and H. Yin · 2023
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Graph Condensation via Eigenbasis Matching
Y. Liu, D. Bo, and C. Shi · 2023
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CaT: Balanced Continual Graph Learning with Graph Condensation
Y. Liu, R. Qiu, and Z. Huang · 2023
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PUMA: Efficient Continual Graph Learning with Graph Condensation
Y. Liu, R. Qiu, Y. Tang, H. Yin, and Z. Huang · 2023
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FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks
Q. Pan, R. Wu, T. Liu, T. Zhang, Y. Zhu, and W. Wang · 2023
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Condensing Graphs via One-Step Gradient Matching
W. Jin, X. Tang, H. Jiang, Z. Li, D. Zhang, J. Tang, and B. Yin · 2022
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Graph condensation for graph neural networks
W. Jin, L. Zhao, S. Zhang, Y. Liu, J. Tang, and N. Shah · 2022
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Graph Condensation via Receptive Field Distribution Matching
M. Liu, S. Li, X. Chen, and L. Song · 2022
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Rethinking graph neural networks for anomaly detection
J. Tang, J. Li, Z. Gao, and J. Li · 2022
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CAFE: Learning to Condense Dataset by Aligning Features
K. Wang, B. Zhao, X. Peng, Z. Zhu, S. Yang, S. Wang, G. Huang, H. Bilen, X. Wang, and Y. You · 2022
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Fair graph distillation
Q. Feng, Z. S. Jiang, R. Li, Y. Wang, N. Zou, J. Bian, and X. Hu · 2023
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Kernel Ridge Regression-Based Graph Dataset Distillation
Z. Xu, Y. Chen, M. Pan, H. Chen, M. Das, H. Yang, and H. Tong · 2023
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Does graph distillation see like vision dataset counterpart?
B. Yang, K. Wang, Q. Sun, C. Ji, X. Fu, H. Tang, Y. You, and J. Li · 2023
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Dataset Condensation with Distribution Matching
B. Zhao and H. Bilen · 2023
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Structure-free graph condensation: From large-scale graphs to condensed graph-free data
X. Zheng, M. Zhang, C. Chen, Q. V. H. Nguyen, X. Zhu, and S. Pan · 2023
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A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation
M. Hashemi, S. Gong, J. Ni, W. Fan, B. A. Prakash, and W. Jin · 2024
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Fast graph condensation with structure-based neural tangent kernel
L. Wang, W. Fan, J. Li, Y. Ma, and Q. Li · 2024
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Navigating complexity: Toward lossless graph condensation via expanding window matching
Y. Zhang, T. Zhang, K. Wang, Z. Guo, Y. Liang, X. Bresson, W. Jin, and Y. You · 2024
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