Method evidence record
Dynamic Stochastic Block Model
The Dynamic Stochastic Block Model (DSBM) is a generative probabilistic framework that extends the static stochastic block model to networks observed across multiple time points. It jointly models community membership and community evolution, allowing researchers to detect and track latent groups and their structural changes over time in longitudinal network data.
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Dynamic Stochastic Block Model (Temporal Community Detection)
Taxonomic method record · ml-model / network-analysis
- Yang, T., Chi, Y., Zhu, S., Gong, Y., & Jin, R. (2011). Detecting communities and their evolutions in dynamic social networks — a Bayesian approach. Machine Learning, 82(2), 157–189. · DOI 10.1007/s10994-010-5214-7
- Matias, C., & Miele, V. (2017). Statistical clustering of temporal networks through a dynamic stochastic block model. Journal of the Royal Statistical Society: Series B, 79(4), 1119–1141. · DOI 10.1111/rssb.12200
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