Low-Rank Graphon Learning for Networks

Xinyuan Fan; Feiyan Ma; Chenlei Leng; Weichi Wu

NeurIPS 2025poster0 citations

Low-Rank Graphon Learning for Networks

Xinyuan Fan, Feiyan Ma, Chenlei Leng, Weichi Wu

Abstract

Graphons offer a powerful framework for modeling large-scale networks, yet estimation remains challenging. We propose a novel approach that leverages a low-rank additive representation, yielding both a low-rank connection probability matrix and a low-rank graphon--two goals rarely achieved jointly. Our method resolves identification issues and enables an efficient sequential algorithm based on subgraph counts and interpolation. We establish consistency and demonstrate strong empirical performance in terms of computational efficiency and estimation accuracy through simulations and data analysis.

low-rank graphonsubgraph countsconnection probability matrixnonparametric statisticsnetwork analysis

BibTeX

@inproceedings{
fan2025lowrank,
title={Low-Rank Graphon Learning for Networks},
author={Xinyuan Fan and Feiyan Ma and Chenlei Leng and Weichi Wu},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=1n5TJh3LEb}
}