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Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires considerable human effort.
Active learning for statistical natural language parsing
Min Tang, Xiaoqiang Luo, and Salim Roukos · 2002
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Diverse ensembles for active learning
Prem Melville and Raymond J Mooney · 2004
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Active learning using pre-clustering
Hieu T Nguyen and Arnold Smeulders · 2004
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Active learning for regression based on query by committee
Robert Burbidge, Jem J Rowland, and Ross D King · 2007
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Two-dimensional active learning for image classification
Guo-Jun Qi, Xian-Sheng Hua, Yong Rui, Jinhui Tang, and Hong-Jiang Zhang · 2008
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Active learning with sampling by uncertainty and density for word sense disambiguation and text classification
Jingbo Zhu, Huizhen Wang, Tianshun Yao, and Benjamin K Tsou · 2008
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Why label when you can search? alternatives to active learning for applying human resources to build classification models under extreme class imbalance
Josh Attenberg and Foster Provost · 2010
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Graph neural networks in recommender systems: a survey
Shiwen Wu, Fei Sun, Wentao Zhang, and Bin Cui · 2011
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Active learning: A survey
Charu C Aggarwal, Xiangnan Kong, Quanquan Gu, Jiawei Han, and S Yu Philip · 2014
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Exploring representativeness and informativeness for active learning
Bo Du, Zengmao Wang, Lefei Zhang, Liangpei Zhang, Wei Liu, Jialie Shen, and Dacheng Tao · 2015
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Multi-class active learning by uncertainty sampling with diversity maximization
Yi Yang, Zhigang Ma, Feiping Nie, Xiaojun Chang, and Alexander G Hauptmann · 2015
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Active learning for graph embedding
Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang · 2017
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Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Scalable and parallelizable influence maximization with random walk ranking and rank merge pruning
Seungkeol Kim, Dongeun Kim, Jinoh Oh, Jeong-Hyon Hwang, Wook-Shin Han, Wei Chen, and Hwanjo Yu · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Social influence maximization under empirical influence models
Sinan Aral and Paramveer S Dhillon · 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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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
MCNE: an end-to-end framework for learning multiple conditional network representations of social network
Hao Wang, Tong Xu, Qi Liu, Defu Lian, Enhong Chen, Dongfang Du, Han Wu, and Wen Su · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger · 2019
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Active learning for imbalanced datasets
Umang Aggarwal, Adrian Popescu, and Céline Hudelot · 2020
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Contrastive multi-view representation learning on graphs
Kaveh Hassani and Amir Hosein Khasahmadi · 2020
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Graph policy network for transferable active learning on graphs
Shengding Hu, Zheng Xiong, Meng Qu, Xingdi Yuan, Marc-Alexandre Côté, Zhiyuan Liu, and Jian Tang · 2020
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Seal: Semisupervised adversarial active learning on attributed graphs
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Cost-effective active learning for hierarchical multi-label classification
Yifan Yan and Sheng-Jun Huang · 2018
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Graph transformation policy network for chemical reaction prediction
Kien Do, Truyen Tran, and Svetha Venkatesh · 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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Predicting path failure in time-evolving graphs
Jia Li, Zhichao Han, Hong Cheng, Jiao Su, Pengyun Wang, Jianfeng Zhang, and Lujia Pan · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling
Cited in the paper.
Yayong Li, Jie Yin, and Ling Chen · 2020
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Unifying graph convolutional neural networks and label propagation
Hongwei Wang and Jure Leskovec · 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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On graph neural networks versus graph-augmented mlps
Lei Chen, Zhengdao Chen, and Joan Bruna · 2021
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Bootstrapping for batch active sampling
Heinrich Jiang and Maya R. Gupta · 2021
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ALG: fast and accurate active learning framework for graph convolutional networks
Wentao Zhang, Yu Shen, Yang Li, Lei Chen, Zhi Yang, and Bin Cui · 2021
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