Local weak convergence and its applications
Abstract
Motivated in part by understanding average case analysis of fundamental algorithms in computer science, and in part by the wide array of network data available over the last decade, a variety of random graph models, with corresponding processes on these objects, have been proposed over the last few years. The main goal of this paper is to give an overview of local weak convergence, which has emerged as a major technique for understanding large network asymptotics for a wide array of functionals and models. As opposed to a survey, the main goal is to try to explain some of the major concepts and their use to junior researchers in the field and indicate potential resources for further reading.
Cite
@article{arxiv.2403.01544,
title = {Local weak convergence and its applications},
author = {Sayan Banerjee and Shankar Bhamidi and Jianan Shen and Seth Parker Young},
journal= {arXiv preprint arXiv:2403.01544},
year = {2024}
}
Comments
33 pages. Submitted to a special issue in honor of K.R. Parthasarathy