방법 비교

	Latent Space Network Model	사회 연결망 분석
분야	Sociology	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2002	1934 (sociometry); 1994 (modern formalization)
창시자	Peter Hoff, Adrian Raftery & Mark Handcock	Moreno, J.L.; formalized by Wasserman & Faust
유형	Latent-variable model placing actors in an unobserved social space	Structural/relational analysis framework
원전	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.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
별칭	latent space model, latent position model, LSM, latent distance model	SNA, network analysis, sociometric analysis, relational analysis
관련	4	5
요약	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.	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.