2026
From Moments to Models: Graphon-Mixture Learning for Mixup and Contrastive Learning
ICML 2026poster
Real-world graph datasets often arise from mixtures of populations, where graphs are generated by multiple distinct underlying distributions. In this work, we propose a unified framework that explicitly models graph data as a mixture of probabilistic graph generative models represented by graphons. …