Linganisha mbinu

	Network Autocorrelation Model	Uchambuzi wa Mitandao ya Kijamii
Nyanja	Sociology	Uchanganuzi wa Mitandao
Familia	Regression model	Machine learning
Mwaka wa asili	1980 (spatial/network models); 2002 (weight matrix)	1934 (sociometry); 1994 (modern formalization)
Mwanzilishi	Patrick Doreian; Roger Leenders (weight-matrix synthesis)	Moreno, J.L.; formalized by Wasserman & Faust
Aina	Regression with an autoregressive term on a network weight matrix	Structural/relational analysis framework
Chanzo asilia	Leenders, R. Th. A. J. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24(1), 21–47.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
Majina mbadala	network effects model, social influence model, network disturbances model, autoregressive network model	SNA, network analysis, sociometric analysis, relational analysis
Zinazohusiana	4	5
Muhtasari	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.