Year: 2024
Type: conference-paper
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.
Author: Kaize Ding
Institution: Northwestern University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4393160701