The same idea has been extended to social networks, where the data consist of the presence or absence (or intensity) of links between actors (Hoff et al 2002). This also allows inference about clustering in social networks to be made at the same time (Handcock, Raftery and Tantrum, 2007). These methods are implemented in the latentnet software.
Friel, N., Wyse, J. and Raftery, A.E. (2016). Interlocking directorates in Irish companies using bipartite networks: a latent space approach. Proceedings of the National Academy of Sciences, 113: 6629-6634.
Raftery, A.E., Niu, X., Hoff, P.D. and Yeung, K.Y. (2012). Fast Inference for the Latent Space Network Model Using a Case-Control Approximate Likelihood. Journal of Computational and Graphical Statistics, 21:909-919.
Krivitsky, P., Handcock, M.S., Raftery, A.E. and Hoff, P. (2009). Representing Degree Distributions, Clustering, and Homophily in Social Networks With Latent Cluster Random Effects Models. Social Networks 31:204-213.
Handcock, M.S., Raftery, A.E. and Tantrum, J. (2007). Model-based clustering for social networks (with Discussion). Journal of the Royal Statistical Society, Series A, 170, 301-354.
Oh, M.-S. and Raftery, A.E. (2007). Model-based Clustering with Dissimilarities: A Bayesian Approach. Journal of Computational and Graphical Statistics, 16, 559-585.
Hoff, P., Raftery, A.E. and Handcock, M.S. (2002). Latent Space Approaches to Social Network Analysis. Journal of the American Statistical Association, 97, 1090-1098.
Oh, M.-S. and Raftery, A.E. (2001). Bayesian Multidimensional Scaling and Choice of Dimension. Journal of the American Statistical Association, 96, 1031-1044.
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Updated May 31, 2017..
Copyright 2005-2017 by Adrian E. Raftery; all rights reserved.