ML Anthology

Data-Efficient Graph Learning

Kaize Ding
AAAI 2024 pp. 22663
doi:10.1609/AAAI.V38I20.30279 /aaai/2024/ding2024aaai-data/

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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Cite

Text

Ding. "Data-Efficient Graph Learning." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I20.30279

Markdown

[Ding. "Data-Efficient Graph Learning." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/ding2024aaai-data/) doi:10.1609/AAAI.V38I20.30279

BibTeX

@inproceedings{ding2024aaai-data,
  title     = {{Data-Efficient Graph Learning}},
  author    = {Ding, Kaize},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {22663},
  doi       = {10.1609/AAAI.V38I20.30279},
  url       = {https://mlanthology.org/aaai/2024/ding2024aaai-data/}
}