เปรียบเทียบวิธี

	Network Autocorrelation Model	การวิเคราะห์เครือข่ายทางสังคม
สาขาวิชา	Sociology	การวิเคราะห์เครือข่าย
ตระกูล	Regression model	Machine learning
ปีกำเนิด	1980 (spatial/network models); 2002 (weight matrix)	1934 (sociometry); 1994 (modern formalization)
ผู้ริเริ่ม	Patrick Doreian; Roger Leenders (weight-matrix synthesis)	Moreno, J.L.; formalized by Wasserman & Faust
ประเภท	Regression with an autoregressive term on a network weight matrix	Structural/relational analysis framework
แหล่งต้นตำรับ	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
ชื่อเรียกอื่น	network effects model, social influence model, network disturbances model, autoregressive network model	SNA, network analysis, sociometric analysis, relational analysis
ที่เกี่ยวข้อง	4	5
สรุป	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.