Representing Degree Distributions, Clustering, and Homophily in Social Networks With Latent Cluster Random Effects Models

By Pavel N. Krivitsky, Mark S. Handcock, Adrian E. Raftery, Peter D. Hoff. Soc. Networks, 2009

319 citations recorded by the source corpus.

Social network data often involve transitivity, homophily on observed attributes, clustering, and heterogeneity of actor degrees. We propose a latent cluster random effects model to represent all of these features, and we describe a Bayesian estimation method for it. The model is applicable to both binary and non-binary network data. We illustrate the model using two real datasets. We also apply it to two simulated network datasets with the same, highly skewed, degree distribution, but very different network behavior: one unstructured and the other with transitivity and clustering. Models based on degree distributions, such as scale-free, preferential attachment and power-law models, cannot distinguish between these very different situations, but our model does.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/9d13a684a2d71e7f3bb7f692dd3bf4d961826b96

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Citations in the corpus

Dynamics and Control of Diseases in Networks with Community Structure (2010, background): “Despite the demonstration that social networks show significant community structure [15,16,17,18], and that social processes such as homophily and transitivity result in highly clustered and modular networks [19], the effect of such microstructures on epidemic dynamics has only recently started to b”
A Survey of Statistical Network Models (2009, methodology): “[146] with applications to social network analysis, and has been recently extended in a number of directions to include treatment of transitivity, homophily on node-specific attributes, clustering, and heterogeneity of nodes [144; 137; 183].”
An Econometric Model of Network Formation With Degree Heterogeneity (2017, background): “7Prior empirical work based on a single network has generally taken a Bayesian approach (e.g., van Duijn, Snijders, and Zijlstra (2004), Krivitsky et al. (2009), Christakis et al. (2010), Mele (2013), Goldsmith-Pinkham and Imbens (2013)).”