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Graphs are widely used to model the relational structure of data, and the research of graph machine learning (ML) has a wide spectrum of applications ranging from drug design in molecular graphs to friendship recommendation in social networks.
Arnetminer: extraction and mining of academic social networks
Tang, J.; Zhang, J.; Yao, L.; Li, J.; Zhang, L.; and Su, Z. 2008 · 2008
Earlier work this paper cites.
Heterogeneous network embedding via deep architectures
Chang, S.; Han, W.; Tang, J.; Qi, G.-J.; Aggarwal, C. C.; and Huang, T. S. 2015 · 2015
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Inferring networks of substitutable and complementary products
McAuley, J.; Pandey, R.; and Leskovec, J. 2015 · 2015
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Deep neural networks for learning graph representations
Cao, S.; Lu, W.; and Xu, Q. 2016 · 2016
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Matching networks for one shot learning
Vinyals, O.; Blundell, C.; Lillicrap, T.; Wierstra, D.; et al. 2016 · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C.; Abbeel, P.; and Levine, S. 2017 · 2017
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Inductive representation learning on large graphs
Hamilton, W.; Ying, Z.; and Leskovec, J. 2017 · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2017 · 2017
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Prototypical networks for few-shot learning
Snell, J.; Swersky, K.; and Zemel, R. 2017 · 2017
Earlier work this paper cites.
Using trusted data to train deep networks on labels corrupted by severe noise
Hendrycks, D.; Mazeika, M.; Wilson, D.; and Gimpel, K. 2018 · 2018
Earlier work this paper cites.
Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Jiang, L.; Zhou, Z.; Leung, T.; Li, L.-J.; and Fei-Fei, L. 2018 · 2018
Cited alongside, same era.
Graph attention networks
Veličković, P.; Cucurull, G.; Casanova, A.; Romero, A.; Lio, P.; and Bengio, Y. 2018 · 2018
Cited alongside, same era.
FEW-SHOT LEARNING ON GRAPHS VIA SUPER-CLASSES BASED ON GRAPH SPECTRAL MEASURES
Chauhan, J.; Nathani, D.; and Kaul, M. 2019 · 2019
Cited alongside, same era.
Understanding and utilizing deep neural networks trained with noisy labels
Chen, P.; Liao, B. B.; Chen, G.; and Zhang, S. 2019 · 2019
Cited alongside, same era.
Deep anomaly detection on attributed networks
Ding, K.; Li, J.; Bhanushali, R.; and Liu, H. 2019 · 2019
Cited alongside, same era.
Simplifying graph convolutional networks
Wu, F.; Zhang, T.; Souza Jr, A. H. d.; Fifty, C.; Yu, T.; and Weinberger, K. Q. 2019 · 2019
Graph prototypical networks for few-shot learning on attributed networks
Ding, K.; Wang, J.; Li, J.; Shu, K.; Liu, C.; and Liu, H. 2020 · 2020
Later among the works it cites.
Graph meta learning via local subgraphs
Huang, K.; and Zitnik, M. 2020 · 2020
Later among the works it cites.
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
Lan, L.; Wang, P.; Du, X.; Song, K.; Tao, J.; and Guan, X. 2020 · 2020
Later among the works it cites.
Towards locality-aware meta-learning of tail node embeddings on networks
Liu, Z.; Zhang, W.; Fang, Y.; Zhang, X.; and Hoi, S. C. 2020 · 2020
Later among the works it cites.
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification
Ma, N.; Bu, J.; Yang, J.; Zhang, Z.; Yao, C.; Yu, Z.; Zhou, S.; and Yan, X. 2020 · 2020
Later among the works it cites.
Graph few-shot learning via knowledge transfer
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Cited alongside, same era.
Variational few-shot learning
Zhang, J.; Zhao, C.; Ni, B.; Xu, M.; and Yang, X. 2019 · 2019
Cited alongside, same era.
Metacleaner: Learning to hallucinate clean representations for noisy-labeled visual recognition
Zhang, W.; Wang, Y.; and Qiao, Y. 2019 · 2019
Cited alongside, same era.
Meta-GNN: On Few-shot Node Classification in Graph Meta-learning
Zhou, F.; Cao, C.; Zhang, K.; Trajcevski, G.; Zhong, T.; and Geng, J. 2019 · 2019
Cited alongside, same era.
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
Baek, J.; Lee, D. B.; and Hwang, S. J. 2020 · 2020
Cited alongside, same era.
Meta-learning for semi-supervised few-shot classification
Ren, M.; Triantafillou, E.; Ravi, S.; Snell, J.; Swersky, K.; Tenenbaum, J. B.; Larochelle, H.; and Zemel, R. S. 2018a
Cited in the paper.
Learning to reweight examples for robust deep learning
Ren, M.; Zeng, W.; Yang, B.; and Urtasun, R. 2018b
Cited in the paper.
Yao, H.; Zhang, C.; Wei, Y.; Jiang, M.; Wang, S.; Huang, J.; Chawla, N. V.; and Li, Z. 2020 · 2020
Later among the works it cites.
Few-shot knowledge graph completion
Zhang, C.; Yao, H.; Huang, C.; Jiang, M.; Li, Z.; and Chawla, N. V. 2020 · 2020
Later among the works it cites.
On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
Dong, H.; Chen, J.; Feng, F.; He, X.; Bi, S.; Ding, Z.; and Cui, P. 2021 · 2021
Closest in time.
Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph
Liu, Z.; Fang, Y.; Liu, C.; and Hoi, S. C. 2021 · 2021
Closest in time.
Few-shot Node Classification on Attributed Networks with Graph Meta-learning
Liu, Y.; Li, M.; Li, X.; Giunchiglia, F.; Feng, X.; and Guan, R. 2022 · 2022
Closest in time.