latentnet

Fitting Latent Cluster Models for Networks with latentnet. latentnet is a package to fit and evaluate statistical latent position and cluster models for networks. Hoff, Raftery, and Handcock (2002) suggested an approach to modeling networks based on positing the existence of an latent space of characteristics of the actors. Relationships form as a function of distances between these characteristics as well as functions of observed dyadic level covariates. In latentnet social distances are represented in a Euclidean space. It also includes a variant of the extension of the latent position model to allow for clustering of the positions developed in Handcock, Raftery, and Tantrum (2007). The package implements Bayesian inference for the models based on an Markov chain Monte Carlo algorithm. It can also compute maximum likelihood estimates for the latent position model and a two-stage maximum likelihood method for the latent position cluster model. For latent position cluster models, the package provides a Bayesian way of assessing how many groups there are, and thus whether or not there is any clustering (since if the preferred number of groups is 1, there is little evidence for clustering). It also estimates which cluster each actor belongs to. These estimates are probabilistic, and provide the probability of each actor belonging to each cluster. It computes four types of point estimates for the coefficients and positions: maximum likelihood estimate, posterior mean, posterior mode and the estimator which minimizes Kullback-Leibler divergence from the posterior. You can assess the goodness-of-fit of the model via posterior predictive checks. It has a function to simulate networks from a latent position or latent position cluster model.

This software is also peer reviewed by journal JSS.


References in zbMATH (referenced in 11 articles , 1 standard article )

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  1. Kim, Bomin; Lee, Kevin H.; Xue, Lingzhou; Niu, Xiaoyue: A review of dynamic network models with latent variables (2018)
  2. Mair, Patrick: Modern psychometrics with R (2018)
  3. Michael Schweinberger; Pamela Luna: hergm: Hierarchical Exponential-Family Random Graph Models (2018) not zbMATH
  4. Latouche, Pierre; Birmelé, Etienne; Ambroise, Christophe: Model selection in overlapping stochastic block models (2014)
  5. Olson, Jamie F.; Carley, Kathleen M.: Exact and approximate EM estimation of mutually exciting Hawkes processes (2013)
  6. Salter-Townshend, Michael; Murphy, Thomas Brendan: Variational Bayesian inference for the latent position cluster model for network data (2013)
  7. Latouche, Pierre; Birmelé, Etienne; Ambroise, Christophe: Overlapping stochastic block models with application to the French political blogosphere (2011)
  8. Gormley, Isobel Claire; Murphy, Thomas Brendan: A mixture of experts latent position cluster model for social network data (2010)
  9. Mark Handcock; David Hunter; Carter Butts; Steven Goodreau: Martina Morris: statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data (2008) not zbMATH
  10. Pavel Krivitsky; Mark Handcock: Fitting Latent Cluster Models for Networks with latentnet (2008) not zbMATH
  11. Chris Fraley; Adrian Raftery: Model-based Methods of Classification: Using the mclust Software in Chemometrics (2007) not zbMATH