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

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  • Luca Brusa

    (University of Milano-Bicocca)

  • Fulvia Pennoni

    (University of Milano-Bicocca)

Abstract

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.

Suggested Citation

  • Luca Brusa & Fulvia Pennoni, 2025. "Variational inference for estimating dynamic stochastic block models through an evolutionary algorithm," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 19(2), pages 469-492, June.
    • Handle: RePEc:spr:advdac:v:19:y:2025:i:2:d:10.1007_s11634-025-00634-9
    DOI: 10.1007/s11634-025-00634-9
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    References listed on IDEAS

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    1. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    2. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    3. Sharon M. McNicholas & Paul D. McNicholas & Daniel A. Ashlock, 2021. "An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 264-279, July.
    4. Santiago Olivella & Tyler Pratt & Kosuke Imai, 2022. "Dynamic Stochastic Blockmodel Regression for Network Data: Application to International Militarized Conflicts," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(539), pages 1068-1081, September.
    5. Catherine Matias & Vincent Miele, 2017. "Statistical clustering of temporal networks through a dynamic stochastic block model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1119-1141, September.
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