# Basic models and questions in statistical network analysis

arXiv: https://arxiv.org/abs/1609.03511 · HTML: https://mlsanity.com/paper/1609.03511/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2016.

## Built on by (most cited first, 1 of 1)

- [Harnessing Multiple Correlated Networks for Exact Community Recovery](https://mlsanity.com/paper/2412.02796/) (2024)

## Abstract (arXiv)

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.