Σύγκριση μεθόδων

	Network Autocorrelation Model	Ανάλυση Κοινωνικών Δικτύων
Πεδίο	Sociology	Ανάλυση Δικτύων
Οικογένεια	Regression model	Machine learning
Έτος προέλευσης	1980 (spatial/network models); 2002 (weight matrix)	1934 (sociometry); 1994 (modern formalization)
Δημιουργός	Patrick Doreian; Roger Leenders (weight-matrix synthesis)	Moreno, J.L.; formalized by Wasserman & Faust
Τύπος	Regression with an autoregressive term on a network weight matrix	Structural/relational analysis framework
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	network effects model, social influence model, network disturbances model, autoregressive network model	SNA, network analysis, sociometric analysis, relational analysis
Συναφείς	4	5
Σύνοψη	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.