Data-Efficient Graph Learning

Authors

  • Kaize Ding Northwestern University

DOI:

https://doi.org/10.1609/aaai.v38i20.30279

Keywords:

Data-Efficient Learning, Graph Machine Learning, Robust And Reliable Machine Learning

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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Published

2024-03-24

How to Cite

Ding, K. (2024). Data-Efficient Graph Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 38(20), 22663-22663. https://doi.org/10.1609/aaai.v38i20.30279