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Self-supervised learning provides a promising path towards eliminating the need for costly label information in representation learning on graphs.
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William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Big self-supervised models are strong semi-supervised learners
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Wiki-cs: A wikipedia-based benchmark for graph neural networks, 2020
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Thomas N. Kipf and Max Welling · 2017
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Adamw and super-convergence is now the fastest way to train neural nets
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Graph Representation Learning via Graphical Mutual Information Maximization , pp. 259–270
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Large-scale graph representation learning with very deep gnns and self-supervision
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