方法对比

	Stochastic Actor-Oriented Model	Latent Space Network Model
领域	Sociology	Sociology
方法族	Machine learning	Machine learning
起源年份	2001	2002
提出者	Tom A. B. Snijders	Peter Hoff, Adrian Raftery & Mark Handcock
类型	Continuous-time model for longitudinal network and behavior dynamics	Latent-variable model placing actors in an unobserved social space
开创性文献	Snijders, T. A. B. (2001). The statistical evaluation of social network dynamics. Sociological Methodology, 31(1), 361–395.	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.
别名	SAOM, actor-based model, stochastic actor-based model, SIENA model	latent space model, latent position model, LSM, latent distance model
相关	4	4
摘要	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.	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.