We develop a method to identify statistically significant communities in a weighted network with a high proportion of self-looping weights. We use this method to find overlapping agglomerations of U.S. counties by representing inter-county commuting as a weighted network. We identify three types of communities; non-nodal, nodal and monads, which correspond to different types of regions. The results suggest that traditional regional delineations that rely on ad hoc thresholds do not account for important and pervasive connections that extend far beyond expected metropolitan boundaries or megaregions.
@article{arxiv.1903.06029,
title = {Demarcating Geographic Regions using Community Detection in Commuting Networks with Significant Self-Loops},
author = {Mark He and Joseph Glasser and Nathaniel Pritchard and Shankar Bhamidi and Nikhil Kaza},
journal= {arXiv preprint arXiv:1903.06029},
year = {2020}
}