GRLC: Graph Representation Learning With Constraints

10.1109/TIP.2021.3062692

10.24963/ijcai.2018/362

10.1109/tnnls.2022.3172588

10.1109/TMM.2018.2889560

10.24963/ijcai.2021/204

van den Oord, 2018, Representation learning with contrastive predictive coding, arXiv:1807.03748

10.1109/TNNLS.2021.3068344

10.1109/TNNLS.2021.3056080

Kipf, Semi-supervised classification with graph convolutional networks, Proc. ICLR, 1

Chen, A simple framework for contrastive learning of visual representations, Proc. ICML, 1597

Lee, Predicting what you already know helps: Provable self-supervised learning, Proc. NeurIPS, 1

Velickovic, Deep graph infomax, Proc. ICLR, 1

Zhu, 2020, Deep graph contrastive representation learning, arXiv:2006.04131

10.1007/978-3-030-58621-8_45

Hassani, Contrastive multi-view representation learning on graphs, Proc. ICML, 4116

10.18653/v1/2021.emnlp-main.552

Chen, Big self-supervised models are strong semi-supervised learners, Proc. NeurIPS, 1

You, Graph contrastive learning with augmentations, Proc. NeurIPS, 1

10.1109/TMI.2022.3201974

10.1109/TKDE.2019.2911946

10.1109/TKDE.2020.3048678

Jin, 2020, Self-supervised learning on graphs: Deep insights and new direction, arXiv:2006.10141

10.1109/tkde.2021.3090866

10.1609/aaai.v35i12.17293

10.1016/j.ipm.2021.102733

10.1016/j.inffus.2021.07.013

10.1109/TNNLS.2020.2986029

10.1109/CVPR42600.2020.00975

Devlin, BERT: Pre-training of deep bidirectional transformers for language understanding, Proc. NAACL, 1

10.18653/v1/P19-1214

Saunshi, A theoretical analysis of contrastive unsupervised representation learning, Proc. ICML, 5628

Wang, Understanding contrastive representation learning through alignment and uniformity on the hypersphere, Proc. ICML, 9929

Tsai, 2020, Demystifying self-supervised learning: An information-theoretical framework, arXiv:2006.05576

Tosh, Contrastive learning, multi-view redundancy, and linear models, Proc. ALT, 1179

Sun, Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization, Proc. ICLR, 1

Mavromatis, 2020, Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning, arXiv:2009.06946

10.1145/3394486.3403168

Ma, 2021, Improving graph representation learning by contrastive regularization, arXiv:2101.11525

Hwang, Self-supervised auxiliary learning with meta-paths for heterogeneous graphs, Proc. NeurIPS, 33, 10294

Opolka, 2019, Spatio-temporal deep graph infomax, arXiv:1904.06316

10.1145/3366423.3380112

10.1016/j.aiopen.2021.01.001

Velickovic, Graph attention networks, Proc. ICLR, 1

10.1145/3394486.3403237

10.1609/aaai.v35i18.17986

Khosla, Supervised contrastive learning, Proc. NeurIPS, 1

10.1109/CVPR.2019.00521

10.1145/3292500.3330836

10.1109/CVPR.2015.7298682

10.1145/2623330.2623732

10.1609/aimag.v29i3.2157

10.1109/tnnls.2022.3161030

Bojchevski, Deep Gaussian embedding of graphs: Unsupervised inductive learning via ranking, Proc. ICLR, 1

Mernyei, 2020, Wiki-CS: A wikipedia-based benchmark for graph neural networks, arXiv:2007.02901

Rozemberczki, 2019, Multi-scale attributed node embedding, arXiv:1909.13021

10.1145/2766462.2767755

Weihua, Open graph benchmark: Datasets for machine learning on graphs, Proc. NeurIPS, 1

Maas, Rectifier nonlinearities improve neural network acoustic models, ICML, 30, 3

Glorot, Understanding the difficulty of training deep feedforward neural networks, Proc. AISTATS, 249

10.1145/3326362

Chen, Simple and deep graph convolutional networks, Proc. Int. Conf. Mach. Learn., 1725

Du, 2017, Topology adaptive graph convolutional networks, arXiv:1710.10370

Hamilton, Inductive representation learning on large graphs, Proc. NIPS, 1025

Defferrard, Convolutional neural networks on graphs with fast localized spectral filtering, Proc. Adv. Neural Inf. Process. Syst., 29, 1