15 citations · 32 across the 6 of their papers we have counts for
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cs.LG2024★ 15 cited
Graph Domain Adaptation: Challenges, Progress and Prospects
Boshen Shi, Yongqing Wang, Fangda Guo +3
As graph representation learning often suffers from label scarcity problems in real-world applications, researchers have proposed graph domain adaptation (GDA) as an effective know…
cs.LG2023★ 11 cited
Causality and Independence Enhancement for Biased Node Classification
Guoxin Chen, Yongqing Wang, Fangda Guo +4
Most existing methods that address out-of-distribution (OOD) generalization for node classification on graphs primarily focus on a specific type of data biases, such as label selec…