Low-Rank Graphon Learning for Networks
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.
Cite
Text
Fan et al. "Low-Rank Graphon Learning for Networks." Advances in Neural Information Processing Systems, 2025.Markdown
[Fan et al. "Low-Rank Graphon Learning for Networks." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/fan2025neurips-lowrank/)BibTeX
@inproceedings{fan2025neurips-lowrank,
title = {{Low-Rank Graphon Learning for Networks}},
author = {Fan, Xinyuan and Ma, Feiyan and Leng, Chenlei and Wu, Weichi},
booktitle = {Advances in Neural Information Processing Systems},
year = {2025},
url = {https://mlanthology.org/neurips/2025/fan2025neurips-lowrank/}
}