Publication Type
Journal Article
Version
publishedVersion
Publication Date
8-2021
Abstract
Predictions of node categories are commonly used to estimate homophily and other relational properties in networks. However, little is known about the validity of using predictions for this task. We show that estimating homophily in a network is a problem of predicting categories of dyads (edges) in the graph. Homophily estimates are unbiased when predictions of dyad categories are unbiased. Node-level prediction models, such as the use of names to classify ethnicity or gender, do not generally produce unbiased predictions of dyad categories and therefore produce biased homophily estimates. Bias comes from three sources: sampling bias, correlation between model errors and node degree, and correlation between node-level model errors along dyads. We examine three methods for estimating homophily: predicting node categories, predicting dyad categories, and a hybrid “ego–alter” approach. This analysis indicates that only the dyadic prediction approach is unbiased, whereas the node-level approach produces both high bias and high overall error. We find that node-level classification performance is not a reliable indicator of accuracy for homophily. Although this article focuses on a particular version of homophily, results generalize to heterophilous cases and other dyadic measures. We conclude with suggestions for research design. Code for this article is available at https://github.com/georgeberry/autocorr.
Keywords
homophily, networks, machine learning, quantitative methodology
Discipline
Digital Communications and Networking | OS and Networks
Research Areas
Information Systems and Management
Publication
Sociological Science
Volume
8
First Page
285
Last Page
307
ISSN
2330-6696
Identifier
10.15195/v8.a14
Publisher
Society for Sociological Science
Citation
BERRY, George; SIRIANNI, Antonio; WEBER, Ingmar; AN, Jisun; and MACY, Michael.
Estimating homophily in social networks using dyadic predictions. (2021). Sociological Science. 8, 285-307.
Available at: https://ink.library.smu.edu.sg/sis_research/6225
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional URL
https://doi.org/10.15195/v8.a14