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Main Authors: Banerjee, Sayan, Bhamidi, Shankar, Shen, Jianan, Young, Seth Parker
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2403.01544
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author Banerjee, Sayan
Bhamidi, Shankar
Shen, Jianan
Young, Seth Parker
author_facet Banerjee, Sayan
Bhamidi, Shankar
Shen, Jianan
Young, Seth Parker
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2403_01544
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Local weak convergence and its applications
Banerjee, Sayan
Bhamidi, Shankar
Shen, Jianan
Young, Seth Parker
Probability
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.
title Local weak convergence and its applications
topic Probability
url https://arxiv.org/abs/2403.01544