On Local Limits of Sparse Random Graphs: Color Convergence and the Refined Configuration Model
Alexander Pluska, SAGAR MALHOTRA
Abstract
Local convergence has emerged as a fundamental tool for analyzing sparse random graph models. We introduce a new notion of local convergence, _color convergence_, based on the Weisfeiler–Leman algorithm. Color convergence fully characterizes the class of random graphs that are well-behaved in the limit for message-passing graph neural networks. Building on this, we propose the _Refined Configuration Model_ (RCM), a random graph model that generalizes the configuration model. The RCM is universal with respect to local convergence among locally tree-like random graph models, including Erdős–Rényi, stochastic block and configuration models. Finally, this framework enables a complete characterization of the random trees that arise as local limits of such graphs.
BibTeX
@inproceedings{
pluska2025on,
title={On Local Limits of Sparse Random Graphs: Color Convergence and the Refined Configuration Model},
author={Alexander Pluska and SAGAR MALHOTRA},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=nadVPpcMvn}
}