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We study the problem of semi-supervised learning with Graph Neural Networks (GNNs) in an active learning setup.
Active learning for graph neural networks via node feature propagation
Yuexin Wu, Yichong Xu, Aarti Singh, Yiming Yang, and Artur Dubrawski · 1910
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On the surprising behavior of distance metrics in high dimensional space
Charu C Aggarwal, Alexander Hinneburg, and Daniel A Keim · 2001
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Finding community structure in very large networks
Aaron Clauset, M. E. J. Newman, and Cristopher Moore · 2004
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Fast algorithm for detecting community structure in networks
M. E. J. Newman · 2004
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Learning with local and global consistency
Dengyong Zhou, Olivier Bousquet, Thomas N Lal, Jason Weston, and Bernhard Schölkopf · 2004
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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 · 2005
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Optimal Design of Experiments
Friedrich Pukelsheim · 2006
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K-means++: The advantages of careful seeding
David Arthur and Sergei Vassilvitskii · 2007
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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An analysis of active learning strategies for sequence labeling tasks
Burr Settles and Mark Craven · 2008
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Finding groups in data: an introduction to cluster analysis , volume 344
Leonard Kaufman and Peter J Rousseeuw · 2009
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A simple and fast algorithm for k-medoids clustering
Hae-Sang Park and Chi-Hyuck Jun · 2009
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Active learning for networked data
Mustafa Bilgic, Lilyana Mihalkova, and Lise Getoor · 2010
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E. Hinton · 2010
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Towards active learning on graphs: An error bound minimization approach
Quanquan Gu and Jiawei Han · 2012
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A variance minimization criterion to active learning on graphs
Ming Ji and Jiawei Han · 2012
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Active learning
Burr Settles · 2012
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Active nearest neighbors in changing environments
Christopher Berlind and Ruth Urner · 2015
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S2: An efficient graph based active learning algorithm with application to nonparametric classification
Gautam Dasarathy, Robert Nowak, and Xiaojin Zhu · 2015
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Activehne: Active heterogeneous network embedding
Xia Chen, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Zhao Li, and Xiangliang Zhang · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2019
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Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang · 2017
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A meta-learning approach to one-step active learning
Gabriella Contardo, Ludovic Denoyer, and Thierry Artières · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Günnemann · 2018
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Active discriminative network representation learning
Li Gao, Hong Yang, Chuan Zhou, Jia Wu, Shirui Pan, and Yue Hu · 2018
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Near-optimal discrete optimization for experimental design: A regret minimization approach
Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, and Yining Wang · 2020
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Seal: Semisupervised adversarial active learning on attributed graphs
Yayong Li, Jie Yin, and Ling Chen · 2020
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Active learning for node classification: The additional learning ability from unlabelled nodes
Juncheng Liu, Yiwei Wang, Bryan Hooi, Renchi Yang, and Xiaokui Xiao · 2020
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Active learning on attributed graphs via graph cognizant logistic regression and preemptive query generation
Florence Regol, Soumyasundar Pal, Yingxue Zhang, and Mark Coates · 2020
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A survey of deep active learning
Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen, and Xin Wang · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
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Subgroup generalization and fairness of graph neural networks
Jiaqi Ma, Junwei Deng, and Qiaozhu Mei · 2021
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