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<p>Dynamic temporal networks are important structures to capture node dependencies and their evolution over time. The dynamic stochastic block model, commonly used with longitudinal network data, is estimated maximizing the likelihood function through the variational expectation-maximization (VEM) algorithm. However, maximization is challenging due to the presence of multiple local maxima. In this paper, we first conduct a simulation study to assess the performance of six different parameter initialization strategies. Second, we introduce a novel specification of the VEM through a genetic algorithm, enabling a more comprehensive exploration of the parameter space. Results from both simulations and historical data on infectious disease transmission highlight the advantages of this approach in overcoming convergence to local maxima and improving node clustering in temporal network data.</p>

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Variational inference for estimating dynamic stochastic block models through an evolutionary algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月23日

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  • Luca Brusa,
  • Fulvia Pennoni

摘要

Dynamic temporal networks are important structures to capture node dependencies and their evolution over time. The dynamic stochastic block model, commonly used with longitudinal network data, is estimated maximizing the likelihood function through the variational expectation-maximization (VEM) algorithm. However, maximization is challenging due to the presence of multiple local maxima. In this paper, we first conduct a simulation study to assess the performance of six different parameter initialization strategies. Second, we introduce a novel specification of the VEM through a genetic algorithm, enabling a more comprehensive exploration of the parameter space. Results from both simulations and historical data on infectious disease transmission highlight the advantages of this approach in overcoming convergence to local maxima and improving node clustering in temporal network data.