Comparer des méthodes

	Latent Space Network Model	Stochastic Actor-Oriented Model
Domaine	Sociology	Sociology
Famille	Machine learning	Machine learning
Année d'origine	2002	2001
Auteur d'origine	Peter Hoff, Adrian Raftery & Mark Handcock	Tom A. B. Snijders
Type	Latent-variable model placing actors in an unobserved social space	Continuous-time model for longitudinal network and behavior dynamics
Source fondatrice	Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090–1098.	Snijders, T. A. B. (2001). The statistical evaluation of social network dynamics. Sociological Methodology, 31(1), 361–395.
Alias	latent space model, latent position model, LSM, latent distance model	SAOM, actor-based model, stochastic actor-based model, SIENA model
Apparentées	4	4
Résumé	The latent space network model represents each actor as a point in an unobserved low-dimensional 'social space' and makes the probability of a tie between two actors a decreasing function of the distance between their points. Introduced by Peter Hoff, Adrian Raftery, and Mark Handcock in 2002, it gives social networks a geometric interpretation in which proximity captures unobserved similarity, and it automatically reproduces transitivity and homophily through the geometry.	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.