Basic models and questions in statistical network analysis
Abstract
Extracting information from large graphs has become an important statistical problem since network data is now common in various fields. In this minicourse we will investigate the most natural statistical questions for three canonical probabilistic models of networks: (i) community detection in the stochastic block model, (ii) finding the embedding of a random geometric graph, and (iii) finding the original vertex in a preferential attachment tree. Along the way we will cover many interesting topics in probability theory such as P\'olya urns, large deviation theory, concentration of measure in high dimension, entropic central limit theorems, and more.
Cite
@article{arxiv.1609.03511,
title = {Basic models and questions in statistical network analysis},
author = {Miklos Z. Racz and Sébastien Bubeck},
journal= {arXiv preprint arXiv:1609.03511},
year = {2016}
}
Comments
38 pages, 10 figures. Lecture notes for a graduate minicourse presented at University of Washington and the XX Brazilian School of Probability in June/July 2016