1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Who Should Teach? Confidence-Aware Dual-Teacher Learning for Few-Shot Node Classification on Text-Attributed Graphs
Hojin Kim, Sujin Yoon, Sungsu Lim +2
Text-Attributed Graphs (TAGs) integrate graph structures and node-associated textual attributes, and recent studies have increasingly leveraged Large Language Models (LLMs) to impr…
cs.LG2026
Quantile-Free Uncertainty Quantification in Graph Neural Networks
Soyoung park, Hwanjun Song, Sungsu Lim
Uncertainty quantification (UQ) in graph neural networks (GNNs) is crucial in high-stakes domains but remains a significant challenge. In graph settings, message passing often reli…
cs.LG2025★ 1 cited
FnRGNN: Distribution-aware Fairness in Graph Neural Network
Soyoung Park, Sungsu Lim
Graph Neural Networks (GNNs) excel at learning from structured data, yet fairness in regression tasks remains underexplored. Existing approaches mainly target classification and re…