ScholarGate
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Regression modelSocial influence / peer effects modeling

Network Autocorrelation Model

The 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.

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Kilder

  1. Leenders, R. Th. A. J. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24(1), 21–47. DOI: 10.1016/S0378-8733(01)00049-1
  2. Doreian, P. (1980). Linear models with spatially distributed data: Spatial disturbances or spatial effects? Sociological Methods & Research, 9(1), 29–60. DOI: 10.1177/004912418000900102

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ScholarGate. (2026, June 22). Network Autocorrelation Model of Social Influence. ScholarGate. https://scholargate.app/no/sociology/network-autocorrelation-model

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ScholarGateNetwork Autocorrelation Model (Network Autocorrelation Model of Social Influence). Hentet 2026-06-24 fra https://scholargate.app/no/sociology/network-autocorrelation-model · Datasett: https://doi.org/10.5281/zenodo.20539026