Informative Pseudo-Labeling for Graph Neural Networks with Few Labels
Informative Pseudo-Labeling for Graph Neural Networks with Few Labels
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Author / Creator
Li, Yayong , Yin, Jie and Chen, Ling
Publisher
Ithaca: Cornell University Library, arXiv.org
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Language
English
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Publisher
Ithaca: Cornell University Library, arXiv.org
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Scope and Contents
Contents
Graph Neural Networks (GNNs) have achieved state-of-the-art results for semi-supervised node classification on graphs. Nevertheless, the challenge of how to effectively learn GNNs with very few labels is still under-explored. As one of the prevalent semi-supervised methods, pseudo-labeling has been proposed to explicitly address the label scarcity...
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Full title
Informative Pseudo-Labeling for Graph Neural Networks with Few Labels
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TN_cdi_proquest_journals_2621811024
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2621811024
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E-ISSN
2331-8422
