Time-varying degree-corrected stochastic block models

Mengxue Li, Rainer von Sachs () and Eugen Pircalabelu
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Mengxue Li: Université catholique de Louvain, LIDAM/ISBA, Belgium
Rainer von Sachs: Université catholique de Louvain, LIDAM/ISBA, Belgium
Eugen Pircalabelu: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2026011, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

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.

Keywords: Dynamic network; Community detection; Time-localised profile-likelihood; Nonparametric curve estimation (search for similar items in EconPapers)
Pages: 43
Date: 2026-03-31
Note: In: Scandinavian Journal of Statistics, 2026
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2026011

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