# Loglinear model selection and human mobility

arXiv: https://arxiv.org/abs/1711.02623 · HTML: https://mlsanity.com/paper/1711.02623/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2017.

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

- [Scalable Bayesian Structure Learning for Gaussian Graphical Models Using Marginal Pseudo-likelihood](https://mlsanity.com/paper/2307.00127/) (2023)

## Abstract (arXiv)

Methods for selecting loglinear models were among Steve Fienberg's research interests since the start of his long and fruitful career. After we dwell upon the string of papers focusing on loglinear models that can be partly attributed to Steve's contributions and influential ideas, we develop a new algorithm for selecting graphical loglinear models that is suitable for analyzing hyper-sparse contingency tables. We show how multi-way contingency tables can be used to represent patterns of human mobility. We analyze a dataset of geolocated tweets from South Africa that comprises 46 million latitude/longitude locations of 476,601 Twitter users that is summarized as a contingency table with 214 variables. KEYWORDS: contingency tables, model selection, human mobility, graphical models, Bayesian structural learning, birth-death processes, pseudo-likelihood