Networks beyond Categories: A Computational Approach to Examining Gender Homophily
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| Title: | Networks beyond Categories: A Computational Approach to Examining Gender Homophily |
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| Language: | English |
| Authors: | Chen-Shuo Hong (ORCID |
| Source: | Sociological Methods & Research. 2026 55(2):459-500. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 42 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Social Networks, Artificial Intelligence, Statistical Analysis, Friendship, Sex, Social Theories, Cues, Adolescents, Longitudinal Studies |
| Assessment and Survey Identifiers: | National Longitudinal Study of Adolescent Health |
| DOI: | 10.1177/00491241251321152 |
| ISSN: | 0049-1241 1552-8294 |
| Abstract: | Social networks literature has explored homophily, the tendency to associate with similar others, as a critical boundary-making process contributing to segregated networks along the lines of identities. Yet, social network research generally conceptualizes identities as sociodemographic categories and seldom considers the inherently continuous and heterogeneous nature of differences. Drawing upon the infracategorical model of inequality, this study demonstrates that a computational approach -- combining machine learning and exponential random graph models (ERGMs) -- can capture the role of categorical conformity in network structures. Through a case study of gender segregation in friendships, this study presents a workflow for developing a machine-learning-based gender conformity measure and applying it to guide the social network analysis of cultural matching. Results show that adolescents with similar gender conformity are more likely to form friendships, net of homophily based on categorical gender and other controls, and homophily by gender conformity mediates homophily by categorical gender. The study concludes by discussing the limitations of this computational approach and its unique strengths in enhancing theories on categories, boundaries, and stratification. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1502005 |
| Database: | ERIC |
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| Abstract: | Social networks literature has explored homophily, the tendency to associate with similar others, as a critical boundary-making process contributing to segregated networks along the lines of identities. Yet, social network research generally conceptualizes identities as sociodemographic categories and seldom considers the inherently continuous and heterogeneous nature of differences. Drawing upon the infracategorical model of inequality, this study demonstrates that a computational approach -- combining machine learning and exponential random graph models (ERGMs) -- can capture the role of categorical conformity in network structures. Through a case study of gender segregation in friendships, this study presents a workflow for developing a machine-learning-based gender conformity measure and applying it to guide the social network analysis of cultural matching. Results show that adolescents with similar gender conformity are more likely to form friendships, net of homophily based on categorical gender and other controls, and homophily by gender conformity mediates homophily by categorical gender. The study concludes by discussing the limitations of this computational approach and its unique strengths in enhancing theories on categories, boundaries, and stratification. |
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| ISSN: | 0049-1241 1552-8294 |
| DOI: | 10.1177/00491241251321152 |
