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Many score-based active learning methods have been successfully applied to graph-structured data, aiming to reduce the number of labels and achieve better performance of graph neural networks based on predefined score functions.
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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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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A variance minimization criterion to active learning on graphs
Ming Ji and Jiawei Han · 2012
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Selective sampling on graphs for classification
Quanquan Gu, Charu Aggarwal, Jialu Liu, and Jiawei Han · 2013
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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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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Active learning for graph embedding
H. Cai, Vincent W Zheng, and C. C. Chang · 2017
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Active learning for graph embedding
Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Active discriminative network representation learning
L. Gao, H. Yang, C. Zhou, J. Wu, and Y. Hu · 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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Activehne: Active heterogeneous network embedding
Xia Chen, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Zhao Li, and Xiangliang Zhang · 2019
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Hyperbolic graph neural networks
Qi Liu, Maximilian Nickel, and Douwe Kiela · 2019
Cited alongside, same era.
Gnnexplainer: Generating explanations for graph neural networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
Cited alongside, same era.
Position-aware graph neural networks
Jiaxuan You, Rex Ying, and Jure Leskovec · 2019
Cited alongside, same era.
Graph convolutional networks: a comprehensive review
Si Zhang, Hanghang Tong, Jiejun Xu, and Ross Maciejewski · 2019
Cited alongside, same era.
Generalization and representational limits of graph neural networks
Vikas Garg, Stefanie Jegelka, and Tommi Jaakkola · 2020
Cited alongside, same era.
Asgn: An active semi-supervised graph neural network for molecular property prediction
Zhongkai Hao, Chengqiang Lu, Zhenya Huang, Hao Wang, Zheyuan Hu, Qi Liu, Enhong Chen, and Cheekong Lee · 2020
Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 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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Flow network based generative models for non-iterative diverse candidate generation, 2021
Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio · 2021
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Gflownet foundations, 2021
Yoshua Bengio, Tristan Deleu, Edward J. Hu, Salem Lahlou, Mo Tiwari, and Emmanuel Bengio · 2021
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Bootstrapping for batch active sampling
Heinrich Jiang and Maya R Gupta · 2021
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Elastic graph neural networks
Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, and Jiliang Tang · 2021
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Cited alongside, same era.
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
Cited alongside, same era.
Gpt-gnn: Generative pre-training of graph neural networks
Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, and Yizhou Sun · 2020
Cited alongside, same era.
Graph structure learning for robust graph neural networks
Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang · 2020
Cited alongside, same era.
Seal: Semisupervised adversarial active learning on attributed graphs
Yayong Li, Jie Yin, and Ling Chen · 2020
Cited alongside, same era.
Towards deeper graph neural networks
Meng Liu, Hongyang Gao, and Shuiwang Ji · 2020
Cited alongside, same era.
Superglue: Learning feature matching with graph neural networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2020
Cited alongside, same era.
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Rim: Reliable influence-based active learning on graphs
Wentao Zhang, Yexin Wang, Zhenbang You, Meng Cao, Ping Huang, Jiulong Shan, Zhi Yang, and Bin Cui · 2021
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Biological sequence design with gflownets
Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure FP Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, et al · 2022
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Gflowcausal: Generative flow networks for causal discovery
Wenqian Li, Yinchuan Li, Shengyu Zhu, Yunfeng Shao, Jianye Hao, and Yan Pang · 2022
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Deep unsupervised active learning on learnable graphs
Handong Ma, Changsheng Li, Xinchu Shi, Ye Yuan, and Guoren Wang · 2022
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Bayesian learning of causal structure and mechanisms with gflownets and variational bayes
Mizu Nishikawa-Toomey, Tristan Deleu, Jithendaraa Subramanian, Yoshua Bengio, and Laurent Charlin · 2022
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Generative flow networks for discrete probabilistic modeling
Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, and Yoshua Bengio · 2022
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Information gain propagation: a new way to graph active learning with soft labels
Wentao Zhang, Yexin Wang, Zhenbang You, Meng Cao, Ping Huang, Jiulong Shan, Zhi Yang, and Bin Cui · 2022
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A theory of continuous generative flow networks
Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, and Nikolay Malkin · 2023
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Dag matters! gflownets enhanced explainer for graph neural networks
Wenqian Li, Yinchuan Li, Zhigang Li, Jianye Hao, and Yan Pang · 2023
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Cflownets: Continuous control with generative flow networks
Yinchuan Li, Shuang Luo, Haozhi Wang, and Jianye Hao · 2023
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