Conditionally Independent Dyads (CID) network models: A latent variable approach to statistical social network analysis |
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Institution: | 1. Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, United States;2. Department of Population Health, New York University School of Medicine, New York, NY, United States;3. Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, United States |
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Abstract: | Latent variable network models that accommodate edge correlations implicitly, by assuming an underlying latent factor, are increasing in popularity. Although, these models are examples of what is a growing body of research, much of the research is focused on proposing new models or extending others. There has been very little work on unifying the models in a single framework. In this paper, we present a complete framework that organizes existing latent variable network models within an integrative generalized additive model. Our framework is called Conditionally Independent Dyad (CID) models, and includes existing network models that assume dyad (or edge) independence conditional on latent variables and other components in the model. We further discuss practical aspects of model fitting such as posterior parameter estimation via MCMC, identifiability of parameters, approaches to handle missing data and model selection via cross-validation, for the proposed additive CID models. Finally, by presenting several data examples, we illustrate the utility of the proposed framework and provide advice on selecting components for building new CID models. |
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Keywords: | Latent variable network models Generalized additive models Bayesian analysis |
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