Salīdzināt metodes

	Network Autocorrelation Model	Stochastic Actor-Oriented Model
Nozare	Sociology	Sociology
Saime	Regression model	Machine learning
Izcelsmes gads	1980 (spatial/network models); 2002 (weight matrix)	2001
Autors	Patrick Doreian; Roger Leenders (weight-matrix synthesis)	Tom A. B. Snijders
Tips	Regression with an autoregressive term on a network weight matrix	Continuous-time model for longitudinal network and behavior dynamics
Pirmavots	Leenders, R. Th. A. J. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24(1), 21–47.	Snijders, T. A. B. (2001). The statistical evaluation of social network dynamics. Sociological Methodology, 31(1), 361–395.
Citi nosaukumi	network effects model, social influence model, network disturbances model, autoregressive network model	SAOM, actor-based model, stochastic actor-based model, SIENA model
Saistītās	4	4
Kopsavilkums	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.	The stochastic actor-oriented model (SAOM), implemented in the SIENA software, is a framework for analyzing the dynamics of social networks observed at two or more time points. It treats observed network panels as snapshots of an unobserved continuous-time process in which actors, at stochastically timed moments, evaluate their local network and decide whether to create, maintain, or drop a tie so as to improve their position according to an objective function.