Time-varying degree-corrected stochastic block models
(2026) Scandinavian Journal of Statistics : theory and applications — (2026)
Files
ISBA_RP_2026-11.pdf
Open Access - Adobe PDF
- 6.88 MB
Details
- Authors
- 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.
- Affiliations
Citations
Li, M., von Sachs, R., & Pircalabelu, E. (2026). Time-varying degree-corrected stochastic block models. Scandinavian Journal of Statistics : theory and applications. Accepted/in-press. https://hdl.handle.net/2078.5/274268 (Original work published 2026)