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Graph condensation (GC) has recently garnered considerable attention due to its ability to reduce large-scale graph datasets while preserving their essential properties.
Distinguishing enzyme structures from non-enzymes without alignments
Paul D Dobson and Andrew J Doig · 2003
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Comparison of descriptor spaces for chemical compound retrieval and classification
Nikil Wale, Ian A Watson, and George Karypis · 2008
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Herding dynamical weights to learn
Max Welling · 2009
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning
Tao-yang Fu, Wang-Chien Lee, and Zhen Lei · 2017
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Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Deep Anomaly Detection on Attributed Networks
Kaize Ding, Jundong Li, Rohit Bhanushali, and Huan Liu · 2019
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Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
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Geom-gcn: Geometric graph convolutional networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang · 2019
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Attributed graph clustering: A deep attentional embedding approach
Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, and Chengqi Zhang · 2019
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Heterogeneous graph attention network
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu · 2019
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Simplifying graph convolutional networks
Felix Wu, Tianyi Zhang, AmauriH. Souza, Christopher Fifty, Tao Yu, and KilianQ. Weinberger · 2019
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
ChristopherJ. Morris, NilsM. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor K. Prasanna · 2020
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Network schema preserving heterogeneous information network embedding
Jianan Zhao, Xiao Wang, Chuan Shi, Zekuan Liu, and Yanfang Ye · 2020
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Graph condensation for graph neural networks
Fedgkd: Unleashing the power of collaboration in federated graph neural networks
Qiying Pan, Ruofan Wu, Tengfei Liu, Tianyi Zhang, Yifei Zhu, and Weiqiang Wang · 2023
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Kernel ridge regression-based graph dataset distillation
Zhe Xu, Yuzhong Chen, Menghai Pan, Huiyuan Chen, Mahashweta Das, Hao Yang, and Hanghang Tong · 2023
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Does graph distillation see like vision dataset counterpart?
Beining Yang, Kai Wang, Qingyun Sun, Cheng Ji, Xingcheng Fu, Hao Tang, Yang You, and Jianxin Li · 2023
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Structure-free graph condensation: From large-scale graphs to condensed graph-free data
Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, and Shirui Pan · 2023
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Exgc: Bridging efficiency and explainability in graph condensation
Junfeng Fang, Xinglin Li, Yongduo Sui, Yuan Gao, Guibin Zhang, Kun Wang, Xiang Wang, and Xiangnan He · 2024
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Wei Jin, Lingxiao Zhao, Shi-Chang Zhang, Yozen Liu, Jiliang Tang, and Neil Shah · 2021
Cited alongside, same era.
Are we really making much progress?: Revisiting, benchmarking and refining heterogeneous graph neural networks
Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, and Jie Tang · 2021
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Masked label prediction: Unified message passing model for semi-supervised classification, 2021
Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun · 2021
Cited alongside, same era.
Dc-bench: Dataset condensation benchmark
Justin Cui, Ruochen Wang, Si Si, and Cho-Jui Hsieh · 2022
Cited alongside, same era.
Condensing graphs via one-step gradient matching
Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, and Bing Yin · 2022
Cited alongside, same era.
Graph condensation via receptive field distribution matching
Mengyang Liu, Shanchuan Li, Xinshi Chen, and Le Song · 2022
Cited alongside, same era.
Multiple sparse graphs condensation
Jian Gao and Jianshe Wu · 2023
Cited alongside, same era.
Closest in time.
Graph condensation for inductive node representation learning
Xinyi Gao, Tong Chen, Yilong Zang, Wentao Zhang, Quoc Viet Hung Nguyen, Kai Zheng, and Hongzhi Yin · 2024
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Xinyi Gao, Junliang Yu, Wei Jiang, Tong Chen, Wentao Zhang, and Hongzhi Yin · 2024
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Mirage: Model-agnostic graph distillation for graph classification
Mridul Gupta, Sahil Manchanda, Sayan Ranu, and Hariprasad Kodamana · 2024
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Graph condensation via eigenbasis matching
Yang Liu, Deyu Bo, and Chuan Shi · 2024
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Gcondenser: Benchmarking graph condensation
Yilun Liu, Ruihong Qiu, and Zi Huang · 2024
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Fast graph condensation with structure-based neural tangent kernel
Lin Wang, Wenqi Fan, Jiatong Li, Yao Ma, and Qing Li · 2024
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Dd-robustbench: An adversarial robustness benchmark for dataset distillation
Yifan Wu, Jiawei Du, Ping Liu, Yuewei Lin, Wenqing Cheng, and Wei Xu · 2024
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A survey on graph condensation
Hongjia Xu, Liangliang Zhang, Yao Ma, Sheng Zhou, Zhuonan Zheng, and Bu Jiajun · 2024
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Two trades is not baffled: Condense graph via crafting rational gradient matching
Tianle Zhang, Yuchen Zhang, Kun Wang, Kai Wang, Beining Yang, Kaipeng Zhang, Wenqi Shao, Ping Liu, Joey Tianyi Zhou, and Yang You · 2024
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Navigating complexity: Toward lossless graph condensation via expanding window matching
Yuchen Zhang, Tianle Zhang, Kai Wang, Ziyao Guo, Yuxuan Liang, Xavier Bresson, Wei Jin, and Yang You · 2024
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