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Title: GitHub - junxia97/SimGRACE: [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"

Open Graph Title: GitHub - junxia97/SimGRACE: [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"

X Title: GitHub - junxia97/SimGRACE: [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"

Description: [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation" - junxia97/SimGRACE

Open Graph Description: [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation" - junxia97/SimGRACE

X Description: [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation" - junxia97/SimGRACE

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https://github.com/junxia97/SimGRACE#simgrace-a-simple-framework-for-graph-contrastive-learning-without-data-augmentation-www-2022
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentationhttps://arxiv.org/pdf/2202.03104.pdf
https://github.com/junxia97/SimGRACE#overview
https://github.com/junxia97/SimGRACE/blob/main/simgrace.png
https://github.com/junxia97/SimGRACE#dataset-download
TU Datasetshttps://chrsmrrs.github.io/datasets/docs/datasets/
chem datahttp://snap.stanford.edu/gnn-pretrain/data/chem_dataset.zip
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https://github.com/junxia97/SimGRACE#citation
https://github.com/junxia97/SimGRACE#useful-resources-for-pretrained-graphs-models-pgms
A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applicationshttps://arxiv.org/abs/2202.07893v1
A curated list of must-read papers, open-source pretrained models and pretraining datasets.https://github.com/junxia97/awesome-pretrain-on-graphs
https://github.com/junxia97/SimGRACE#reference
Graph Contrastive Learning Automated (ICML 2021)https://github.com/Shen-Lab/GraphCL_Automated
Graph Contrastive Learning with Augmentations (NeurIPS 2020)https://github.com/Shen-Lab/GraphCL
Strategies for Pre-training Graph Neural Networks (ICLR 2020)https://github.com/snap-stanford/pretrain-gnns/
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