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Time-varying degree-corrected stochastic block models

Author

Listed:
  • Li, Mengxue

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • von Sachs, Rainer

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Pircalabelu, Eugen

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

Abstract

Recent interest has emerged in community detection for dynamic networks which are observed along a trajectory of points in time. In this paper, we present a time-varying degree-corrected stochastic block model to fit a dynamic network which allows evolving heterogeneity in the degrees of nodes within a community over time. Considering the influence of the varying time window on the aggregation of network information from different time points, in the parameter estimation, we propose a smoothing-based method to recover time-varying degree parameters and communities. We also provide rates of consistency of our smoothed estimators for degree parameters and communities using a time-localised profile- likelihood approach. Extensive simulation studies and applications to two different real data sets complete our work.

Suggested Citation

  • Li, Mengxue & von Sachs, Rainer & Pircalabelu, Eugen, 2026. "Time-varying degree-corrected stochastic block models," LIDAM Reprints ISBA 2026011, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2026011
    Note: In: Scandinavian Journal of Statistics, 2026
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