方法证据记录
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Dynamic Stochastic Block Model (Temporal Community Detection)
分类方法记录 · 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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