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| Network Autocorrelation Model× | Social Network Analysis× | |
|---|---|---|
| Field≠ | Sociology | Network analysis |
| Family≠ | Regression model | Machine learning |
| Year of origin≠ | 1980 (spatial/network models); 2002 (weight matrix) | 1934 (sociometry); 1994 (modern formalization) |
| Originator≠ | Patrick Doreian; Roger Leenders (weight-matrix synthesis) | Moreno, J.L.; formalized by Wasserman & Faust |
| Type≠ | Regression with an autoregressive term on a network weight matrix | Structural/relational analysis framework |
| Seminal source≠ | Leenders, R. Th. A. J. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24(1), 21–47. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Aliases | network effects model, social influence model, network disturbances model, autoregressive network model | SNA, network analysis, sociometric analysis, relational analysis |
| Related≠ | 4 | 5 |
| Summary≠ | 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. | Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system. |
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