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In this paper, we study the \textit{graph condensation} problem by compressing the large, complex graph into a concise, synthetic representation that preserves the most essential and discriminative information of structure and features.
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Adversarial training for free!
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Bi-gcn: Binary graph convolutional network
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Dataset Distillation by Matching Training Trajectories
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How Powerful are Graph Neural Networks?
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Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers
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Delving into effective gradient matching for dataset condensation
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Graph Condensation for Graph Neural Networks
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Robust optimization as data augmentation for large-scale graphs
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Dataset condensation with contrastive signals
Lee, S.; Chun, S.; Jung, S.; Yun, S.; and Yoon, S. 2022 · 2022
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Dataset distillation via factorization
Liu, S.; Wang, K.; Yang, X.; Ye, J.; and Wang, X. 2022 · 2022
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Inductive Lottery Ticket Learning for Graph Neural Networks
Sui, Y.; Wang, X.; Chen, T.; He, X.; and Chua, T.-S. 2022 · 2022
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Molecular contrastive learning of representations via graph neural networks
Wang, Y.; Wang, J.; Cao, Z.; and Barati Farimani, A. 2022 · 2022
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Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey
Jin, G.; Liang, Y.; Fang, Y.; Huang, J.; Zhang, J.; and Zheng, Y. 2023 · 2023
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Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective
Wang, K.; Liang, Y.; Wang, P.; Wang, X.; Gu, P.; Fang, J.; and Wang, Y. 2023 · 2023
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MoleRec: Combinatorial Drug Recommendation with Substructure-Aware Molecular Representation Learning
Yang, N.; Zeng, K.; Wu, Q.; and Yan, J. 2023 · 2023
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Dataset condensation with distribution matching
Zhao, B.; and Bilen, H. 2023 · 2023
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Graph u-nets
Gao, H.; and Ji, S. 2019 · 2092
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