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| Mô hình Khối Ngẫu nhiên Theo Thời gian× | Mô hình Khối Ngẫu nhiên (Stochastic Block Model - SBM)× | |
|---|---|---|
| Lĩnh vực | Phân tích mạng lưới | Phân tích mạng lưới |
| Họ≠ | Machine learning | Process / pipeline |
| Năm ra đời≠ | 2014–2017 | 1983 |
| Người khởi xướng≠ | Xu, K. S. & Hero, A. O.; Matias, C. & Miele, V. | — |
| Loại≠ | Generative probabilistic model | Probabilistic generative graph model |
| Công trình gốc≠ | 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 ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| Tên gọi khác | TSBM, dynamic stochastic block model, time-varying SBM, evolving block model | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| Liên quan≠ | 4 | 7 |
| Tóm tắt≠ | The Temporal Stochastic Block Model (TSBM) extends the classic Stochastic Block Model to sequences of network snapshots, jointly inferring latent community memberships and how those memberships evolve across time. It combines a generative edge-probability model with a Markov process over block assignments, enabling principled statistical detection of community structure that changes over time. | The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis. |
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