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Graph neural networks (GNNs) enable the analysis of graphs using deep learning, with promising results in capturing structured information in graphs.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Herding dynamical weights to learn: Proceedings of the 26th annual international conference on machine learning, Jun 2009
Max Welling · 2009
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Spectral sparsification of graphs, 2010
Daniel A. Spielman and Shang-Hua Teng · 2010
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Super-samples from kernel herding
Yutian Chen, Max Welling, and Alexander J. Smola · 2012
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2016
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Semi-supervised classification with graph convolutional networks, 2017
Thomas N. Kipf and Max Welling · 2017
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icarl: Incremental classifier and representation learning, 2017
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H. Lampert · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Generative invertible networks (gin): Pathophysiology-interpretable feature mapping and virtual patient generation
Jialei Chen, Yujia Xie, Kan Wang, Zih Huei Wang, Geet Lahoti, Chuck Zhang, Mani A. Vannan, Ben Wang, and Zhen Qian · 2018
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Inductive representation learning on large graphs, 2018
William L. Hamilton, Rex Ying, and Jure Leskovec · 2018
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Graph reduction with spectral and cut guarantees, 2018
Andreas Loukas · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
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Graph embedding with rich information through heterogeneous network, 2018
Guolei Sun and Xiangliang Zhang · 2018
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Active learning for convolutional neural networks: A core-set approach, 2018
Ozan Sener and Silvio Savarese · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank, 2019
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2019
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Shanshan Tang, Bo Li, and Haijun Yu · 2019
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Gradient based sample selection for online continual learning, 2019
Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio · 2019
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Graph condensation for graph neural networks, 2021
Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, and Neil Shah · 2021
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Dataset condensation with distribution matching, 2021
Bo Zhao and Hakan Bilen · 2021
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu · 2021
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Graph learning based recommender systems: A review, 2021
Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao, Francesco Ricci, and Philip S. Yu · 2021
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Survey of image based graph neural networks, 2021
Usman Nazir, He Wang, and Murtaza Taj · 2021
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Graph neural networks for natural language processing: A survey, 2021
Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, and Bo Long · 2021
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Trevor Campbell and Tamara Broderick · 2019
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu · 2019
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Simplifying graph convolutional networks, 2019
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr. au2, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger · 2019
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Scail: Classifier weights scaling for class incremental learning, 2020
Eden Belouadah and Adrian Popescu · 2020
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Coresets via bilevel optimization for continual learning and streaming, 2020
Zalán Borsos, Mojmír Mutný, and Andreas Krause · 2020
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Dataset distillation, 2020
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A. Efros · 2020
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Building a pubmed knowledge graph, 2020
Jian Xu, Sunkyu Kim, Min Song, Minbyul Jeong, Donghyeon Kim, Jaewoo Kang, Justin F. Rousseau, Xin Li, Weijia Xu, Vetle I. Torvik, Yi Bu, Chongyan Chen, Islam Akef Ebeid, Daifeng Li, and Ying Ding · 2020
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Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
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Dataset condensation with distribution matching, 2021
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Open graph benchmark: Datasets for machine learning on graphs, 2021
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2021
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Scaling up graph neural networks via graph coarsening, 2021
Zengfeng Huang, Shengzhong Zhang, Chong Xi, Tang Liu, and Min Zhou · 2021
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Scaling up graph neural networks via graph coarsening
Zengfeng Huang, Shengzhong Zhang, Chong Xi, Tang Liu, and Min Zhou · 2021
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Graph-mlp: Node classification without message passing in graph, 2021
Yang Hu, Haoxuan You, Zhecan Wang, Zhicheng Wang, Erjin Zhou, and Yue Gao · 2021
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