3 papers
stat.ME2026
Decorated graphons for temporal network estimation
Charles Dufour, Sofia C. Olhede
We propose a unified nonparametric framework for modeling time-evolving networks using decorated graphons (also known as probability-graphons): symmetric functions that assign to e…
cs.LG2026
Network Learning with Semi-relaxed Gromov-Wasserstein
Charles Dufour, Ulysse Naepels, Leonardo V. Santoro
Estimating the generative mechanism of large-scale networks is a fundamental challenge in statistical machine learning. It requires the identification of the latent connectivity st…
stat.ME2024
Graphon estimation beyond binary edges: inference for decorated graphs with applications to multiplex and weighted networks
Charles Dufour, Sofia C. Olhede
We introduce the first doubly non-parametric estimation method for decorated graphons, a generalisation of graphons that encodes edge weights, edge types, and other edge-level attr…