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Data-Efficient Graph Learning

  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Association for the Advancement of Artificial Intelligence
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Abstract

My research strives to develop fundamental graph-centric learning algorithms to reduce the need for human supervision in low-resource scenarios. The focus is on achieving effective and reliable data-efficient learning on graphs, which can be summarized into three facets: (1) graph weakly-supervised learning; (2) graph few-shot learning; and (3) graph self-supervised learning.

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Publication details

DOI
10.1609/aaai.v38i20.30279
OpenAlex
W4393160701
Document type
conference-paper
Language
EN
Source
Proceedings of the AAAI Conference on Artificial Intelligence
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