A partially separable model for dynamic valued networks

Yik Lun Kei, Yanzhen Chen and Oscar Hernan Madrid Padilla

Computational Statistics & Data Analysis, 2023, vol. 187, issue C

Abstract: The Exponential-family Random Graph Model (ERGM) is a powerful model to fit networks with complex structures. However, for dynamic valued networks whose observations are matrices of counts that evolve over time, the development of the ERGM framework is still in its infancy. To facilitate the modeling of dyad value increment and decrement, a Partially Separable Temporal ERGM is proposed for dynamic valued networks. The parameter learning algorithms inherit state-of-the-art estimation techniques to approximate the maximum likelihood, by drawing Markov chain Monte Carlo (MCMC) samples conditioning on the valued network from the previous time step. The ability of the proposed model to interpret network dynamics and forecast temporal trends is demonstrated with real data.

Keywords: Temporal exponential-family random graph model; Temporal weighted networks; Markov chain Monte Carlo; Maximum likelihood estimation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:187:y:2023:i:c:s0167947323001226

DOI: 10.1016/j.csda.2023.107811

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