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Generative self-supervised learning (SSL), especially masked autoencoders, has become one of the most exciting learning paradigms and has shown great potential in handling graph data.
Self-supervised learning: Generative or contrastive
Liu, X.; Zhang, F.; Hou, Z.; Wang, Z.; Mian, L.; Zhang, J.; and Tang, J. 2020 · 2006
Earlier work this paper cites.
Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks
Li, X.; Ding, D.; Kao, B.; Sun, Y.; and Mamoulis, N. 2020 · 2012
Earlier work this paper cites.
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs
Wang, B.; Li, A.; Li, H.; and Chen, Y. 2020 · 2012
Earlier work this paper cites.
Variational graph auto-encoders
Kipf, T. N.; and Welling, M. 2016 · 2016
Earlier work this paper cites.
metapath2vec: Scalable Representation Learning for Heterogeneous Networks
Dong, Y.; Chawla, N. V.; and Swami, A. 2017 · 2017
Earlier work this paper cites.
Learning graph representations with embedding propagation
Garcia Duran, A.; and Niepert, M. 2017 · 2017
Earlier work this paper cites.
Inductive Representation Learning on Large Graphs
Hamilton, W. L.; Ying, Z.; and Leskovec, J. 2017 · 2017
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
Earlier work this paper cites.
Adversarial Learning on Heterogeneous Information Networks
Hu, B.; Fang, Y.; and Shi, C. 2019 · 2019
Earlier work this paper cites.
Symmetric graph convolutional autoencoder for unsupervised graph representation learning
Park, J.; Lee, M.; Chang, H. J.; Lee, K.; and Choi, J. Y. 2019 · 2019
Earlier work this paper cites.
Heterogeneous Information Network Embedding for Recommendation
Shi, C.; Hu, B.; Zhao, W. X.; and Yu, P. S. 2019 · 2019
Earlier work this paper cites.
Deep Graph Infomax
Velickovic, P.; Fedus, W.; Hamilton, W. L.; Lio, P.; Bengio, Y.; and Hjelm, R. D. 2019 · 2019
Cited alongside, same era.
Heterogeneous Graph Attention Network
Wang, X.; Ji, H.; Shi, C.; Wang, B.; Ye, Y.; Cui, P.; and Yu, P. S. 2019 · 2019
Cited alongside, same era.
Xlnet: Generalized autoregressive pretraining for language understanding
Yang, Z.; Dai, Z.; Yang, Y.; Carbonell, J.; Salakhutdinov, R. R.; and Le, Q. V. 2019 · 2019
Cited alongside, same era.
Heterogeneous Graph Neural Network
Zhang, C.; Song, D.; Huang, C.; Swami, A.; and Chawla, N. V. 2019 · 2019
Cited alongside, same era.
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
Fu, X.; Zhang, J.; Meng, Z.; and King, I. 2020 · 2020
Cited alongside, same era.
Heterogeneous Graph Transformer
Hu, Z.; Dong, Y.; Wang, K.; and Sun, Y. 2020 · 2020
Cited alongside, same era.
Modeling heterogeneous hierarchies with relation-specific hyperbolic cones
Bai, Y.; Ying, Z.; Ren, H.; and Leskovec, J. 2021 · 2021
Later among the works it cites.
Knowledge-preserving incremental social event detection via heterogeneous gnns
Cao, Y.; Peng, H.; Wu, J.; Dou, Y.; Li, J.; and Yu, P. S. 2021 · 2021
Later among the works it cites.
Recipe representation learning with networks
Tian, Y.; Zhang, C.; Metoyer, R.; and Chawla, N. V. 2021 · 2021
Later among the works it cites.
Graph contrastive learning automated
You, Y.; Chen, T.; Shen, Y.; and Wang, Z. 2021 · 2021
Later among the works it cites.
Data augmentation for deep graph learning: A survey
Ding, K.; Xu, Z.; Tong, H.; and Liu, H. 2022 · 2022
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Unsupervised Attributed Multiplex Network Embedding
Park, C.; Kim, D.; Han, J.; and Yu, H. 2020 · 2020
Cited alongside, same era.
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
Qiu, J.; Chen, Q.; Dong, Y.; Zhang, J.; Yang, H.; Ding, M.; Wang, K.; and Tang, J. 2020 · 2020
Cited alongside, same era.
Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark
Yang, C.; Xiao, Y.; Zhang, Y.; Sun, Y.; and Han, J. 2020 · 2020
Cited alongside, same era.
Graph contrastive learning with augmentations
You, Y.; Chen, T.; Sui, Y.; Chen, T.; Wang, Z.; and Shen, Y. 2020 · 2020
Cited alongside, same era.
Network Schema Preserving Heterogeneous Information Network Embedding
Zhao, J.; Wang, X.; Shi, C.; Liu, Z.; and Ye, Y. 2020 · 2020
Cited alongside, same era.
RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation
Tian, Y.; Zhang, C.; Guo, Z.; Huang, C.; Metoyer, R.; and Chawla, N. V. 2022a
Cited in the paper.
Gupta, A.; Tian, S.; Zhang, Y.; Wu, J.; Martin-Martin, R.; and Fei-Fei, L. 2022 · 2022
Closest in time.
Masked autoencoders are scalable vision learners
He, K.; Chen, X.; Xie, S.; Li, Y.; Dollar, P.; and Girshick, R. 2022 · 2022
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GraphMAE: Self-Supervised Masked Graph Autoencoders
Hou, Z.; Liu, X.; Dong, Y.; Wang, C.; Tang, J.; et al. 2022 · 2022
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Graph self-supervised learning: A survey
Liu, Y.; Jin, M.; Pan, S.; Zhou, C.; Zheng, Y.; Xia, F.; and Yu, P. 2022 · 2022
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Large-Scale Representation Learning on Graphs via Bootstrapping
Thakoor, S.; Tallec, C.; Azar, M. G.; Munos, R.; Velickovic, P.; and Valko, M. 2022 · 2022
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A survey on heterogeneous graph embedding: methods, techniques, applications and sources
Wang, X.; Bo, D.; Shi, C.; Fan, S.; Ye, Y.; and Philip, S. Y. 2022 · 2022
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