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Using latent variables to account for heterogeneity in exponential family random graph models

Abstract:
We consider relaxing the homogeneity assumption in exponential family random graph models (ERGMs) using binary latent class indicators. This may be interpreted as combining a posteriori blockmodelling with ERGMs, relaxing the independence assumptions of the former and the homogeneity assumptions of the latter. We propose a Markov chain Monte Carlo algorithm for drawing from the joint posterior of the model parameters and latent class indicators.

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Institution:
University of Oxford
Department:
Politics and Int Relations
Role:
Author
Pages:
pp. 845-849
Publication date:
2010-10-11
URN:
uuid:6670414a-dadb-485b-92ee-07aa8cc0485f
Source identifiers:
474
Local pid:
daisy:474

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