Порівняння методів

	Latent Space Network Model	Blockmodeling
Галузь	Sociology	Sociology
Родина	Machine learning	Process / pipeline
Рік появи	2002	1976
Автор методу	Peter Hoff, Adrian Raftery & Mark Handcock	Harrison White, Scott Boorman & Ronald Breiger
Тип	Latent-variable model placing actors in an unobserved social space	Network partitioning into positions and a reduced role structure
Основоположне джерело	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.	White, H. C., Boorman, S. A., & Breiger, R. L. (1976). Social structure from multiple networks. I. Blockmodels of roles and positions. American Journal of Sociology, 81(4), 730–780.
Інші назви	latent space model, latent position model, LSM, latent distance model	block modeling, blockmodel analysis, generalized blockmodeling, CONCOR
Пов'язані	4	4
Підсумок	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.	Blockmodeling is a family of methods that simplify a social network by partitioning its actors into positions — groups of actors who are equivalent in their pattern of ties — and summarizing the relations between positions as a compact image, or reduced role structure. Introduced by Harrison White, Scott Boorman, and Ronald Breiger in 1976, it shifts attention from individuals to the structural roles they occupy.