UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Network Modularity in the Presence of Covariates

Ehrhardt, B; Wolfe, PJ; (2019) Network Modularity in the Presence of Covariates. SIAM Review , 61 (2) pp. 261-276. 10.1137/17M1111528. Green open access

[img]
Preview
Text
17m1111528.pdf - Published version

Download (7MB) | Preview

Abstract

We characterize the large-sample properties of network modularity in the presence of covariates, under a natural and flexible null model. This provides for the first time an objective measure of whether or not a particular value of modularity is meaningful. In particular, our results quantify the strength of the relation between observed community structure and the interactions in a network. Our technical contribution is to provide limit theorems for modularity when a community assignment is given by nodal features or covariates. These theorems hold for a broad class of network models over a range of sparsity regimes, as well as for weighted, multiedge, and power-law networks. This allows us to assign p-values to observed community structure, which we validate using several benchmark examples from the literature. We conclude by applying this methodology to investigate a multiedge network of corporate email interactions.

Type: Article
Title: Network Modularity in the Presence of Covariates
Open access status: An open access version is available from UCL Discovery
DOI: 10.1137/17M1111528
Publisher version: https://doi.org/10.1137/17M1111528
Language: English
Additional information: Copyright © 2019 SIAM. Published by SIAM under the terms of the Creative Commons 4.0 license (http://creativecommons.org/licenses/by/4.0/).
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10089119
Downloads since deposit
5Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item