Machine learningLatent-variable network inference
Latent Space Network Model
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.
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来源
- 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: 10.1198/016214502388618906 ↗
- Handcock, M. S., Raftery, A. E., & Tantrum, J. M. (2007). Model-based clustering for social networks. Journal of the Royal Statistical Society: Series A, 170(2), 301–354. DOI: 10.1111/j.1467-985X.2007.00471.x ↗
如何引用本页
ScholarGate. (2026, June 22). Latent Space Model for Social Networks. ScholarGate. https://scholargate.app/zh/sociology/latent-space-network-model
选用哪种方法？
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