“…Having interviewed our initial sample we modelled the partial network, using the concepts of latent position network models (Hoff, Raftery, &Handcock, 2002, Wasserman andFaust 1994) and latent position cluster models (Handcock, Raftery, & Tantrum, 2007). A model within two-dimensional latent social space was fitted to the initial interview data, which calculated the probability that individuals were connected.…”
“…Having interviewed our initial sample we modelled the partial network, using the concepts of latent position network models (Hoff, Raftery, &Handcock, 2002, Wasserman andFaust 1994) and latent position cluster models (Handcock, Raftery, & Tantrum, 2007). A model within two-dimensional latent social space was fitted to the initial interview data, which calculated the probability that individuals were connected.…”
“…In particular, Hoff et al (2002) develop the idea of a latent social space and define the probability of a link between two actors as a function of their separation in the latent social space; this idea has been developed in various directions in Handcock et al (2007), Krivitsky and Handcock (2008) and Krivitsky et al (2009) in order to accommodate clusters (or communities) of highly connected actors in the network and other network effects. Airoldi et al (2008) develop an alternative latent variable model for social network data where a soft clustering of network actors is achieved; this has been further extended by Xing et al (2010) to model dynamic networks.…”
Section: Social and Organizational Structurementioning
confidence: 99%
“…The latent position cluster model (LPCM) (Handcock et al 2007) develops the idea of the latent social space model (Hoff et al 2002) by extending the model to accommodate clusters of actors in the latent space. Under the latent position cluster model, the latent location of each actor is assumed to be drawn from a finite normal mixture model, each component of which represents a cluster of actors.…”
Section: A Mixture Of Experts Latent Position Cluster Modelmentioning
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“…Among existing probabilistic social network models, we choose to consider the latent space model proposed by [23] and extended in [18] …”
Section: The Latent Space Model and Its Supervised Versionmentioning
confidence: 99%
“…, Z n have to be estimated for a fixed value of the latent space dimension p which can be chosen by crossvalidation or using a criterion such as BIC. Parameter estimation can be done by iterative likelihood maximization or MCMC techniques (see [23] for details). Recently, an extension of this model for the supervised classification problem has been proposed in [9].…”
Section: The Latent Space Model and Its Supervised Versionmentioning
International audienceIn supervised learning, an important issue usually not taken into account by classical methods is the possibility of having in the test set individuals belonging to a class which has not been observed during the learning phase. Classical supervised algorithms will automatically label such observations as belonging to one of the known classes in the training set and will not be able to detect new classes. This work introduces a model-based discriminant analysis method, called adaptive mixture discriminant analysis (AMDA), which is able to detect several unobserved groups of points and to adapt the learned classifier to the new situation. Two EM-based procedures are proposed for parameter estimation and model selection criteria are used for selecting the actual number of classes. Experiments on artificial and real data demonstrate the ability of the proposed method to deal with complex and real word problems. The proposed approach is also applied to the detection of unobserved communities in social network analysis
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