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stat.ML2026
Semi-Supervised Learning on Graphs using Graph Neural Networks
Juntong Chen, Claire Donnat, Olga Klopp +1
Graph neural networks (GNNs) work remarkably well in semi-supervised node regression, yet a rigorous theory explaining when and why they succeed remains lacking. To address this ga…
stat.ML2025
LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural Networks
So Won Jeong, Claire Donnat
Graph Neural Networks (GNNs) are increasingly used in conjunction with unsupervised learning techniques to learn powerful node representations, but their deployment is hindered by…