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Network Autocorrelation Model×Homophily Analysis×
ÁreaSociologySociology
FamíliaRegression modelProcess / pipeline
Ano de origem1980 (spatial/network models); 2002 (weight matrix)1954 (concept); 2001 (synthesis)
Autor originalPatrick Doreian; Roger Leenders (weight-matrix synthesis)Lazarsfeld & Merton (concept); McPherson, Smith-Lovin & Cook (synthesis)
TipoRegression with an autoregressive term on a network weight matrixMeasurement of similarity-based tie formation
Fonte seminalLeenders, R. Th. A. J. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24(1), 21–47. DOI ↗McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: homophily in social networks. Annual Review of Sociology, 27, 415–444. DOI ↗
Outros nomesnetwork effects model, social influence model, network disturbances model, autoregressive network modelhomophily measurement, assortative mixing analysis, birds-of-a-feather analysis, tie-similarity analysis
Relacionados44
ResumoThe network autocorrelation model adapts spatial-econometric regression to social networks to estimate peer influence: it explains an actor's outcome — an attitude, behavior, or performance — as a function of their own covariates plus a weighted average of their network partners' outcomes. The autocorrelation parameter ρ captures the strength of social influence, and the network weight matrix W encodes who influences whom and how strongly.Homophily analysis quantifies the tendency of similar individuals to form ties — the principle that 'birds of a feather flock together'. It compares the rate at which people connect with others who share an attribute (race, gender, age, education, attitudes) against what would be expected by chance, distinguishing the homophily that arises merely from group sizes from the genuine, behavior-driven preference for similar others.
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ScholarGateComparar métodos: Network Autocorrelation Model · Homophily Analysis. Recuperado em 2026-06-24 de https://scholargate.app/pt/compare