We develop a class of random effect models for dynamic networks aimed to account for a time-varying network topology. Ignoring this aspect may lead to a distortion in the parameter estimate of the model for the edges, since, in principle, an edge is not observable when the related couple of nodes does not belong to the network. A Bayesian conjugate approach is proposed for the inference of this class of models based on the P´olya-Gamma latent variable method. A preliminary simulation study has

Dynamic network models with time-varying nodes / Gherardini L., Bernardi M., Lupparelli M.. - ELETTRONICO. - (2023), pp. 1-6. (Intervento presentato al convegno Statistical Learning, Sustainability and Impact Evaluation).

Dynamic network models with time-varying nodes

Lupparelli M
2023

Abstract

We develop a class of random effect models for dynamic networks aimed to account for a time-varying network topology. Ignoring this aspect may lead to a distortion in the parameter estimate of the model for the edges, since, in principle, an edge is not observable when the related couple of nodes does not belong to the network. A Bayesian conjugate approach is proposed for the inference of this class of models based on the P´olya-Gamma latent variable method. A preliminary simulation study has
2023
Proceedings of the Conference of the Italian Statistical Society
Statistical Learning, Sustainability and Impact Evaluation
Gherardini L., Bernardi M., Lupparelli M.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1324773
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