AAAI 2024technical1 citations
Data-Efficient Graph 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.
BibTeX
@article{Ding_2024, title={Data-Efficient Graph Learning}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30279}, DOI={10.1609/aaai.v38i20.30279}, abstractNote={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.}, number={20}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Ding, Kaize}, year={2024}, month={Mar.}, pages={22663-22663} }