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| Latent Space Network Model× | Anàlisi de Xarxes Socials× | |
|---|---|---|
| Camp≠ | Sociology | Anàlisi de xarxes |
| Família | Machine learning | Machine learning |
| Any d'origen≠ | 2002 | 1934 (sociometry); 1994 (modern formalization) |
| Autor original≠ | Peter Hoff, Adrian Raftery & Mark Handcock | Moreno, J.L.; formalized by Wasserman & Faust |
| Tipus≠ | Latent-variable model placing actors in an unobserved social space | Structural/relational analysis framework |
| Font seminal≠ | 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. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Àlies | latent space model, latent position model, LSM, latent distance model | SNA, network analysis, sociometric analysis, relational analysis |
| Relacionats≠ | 4 | 5 |
| Resum≠ | 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. |
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