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| 시간적 확률 블록 모델× | 확률적 블록 모형 (Stochastic Block Model, SBM)× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열≠ | Machine learning | Process / pipeline |
| 기원 연도≠ | 2014–2017 | 1983 |
| 창시자≠ | Xu, K. S. & Hero, A. O.; Matias, C. & Miele, V. | — |
| 유형≠ | Generative probabilistic model | Probabilistic generative graph model |
| 원전≠ | 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 ↗ |
| 별칭 | TSBM, dynamic stochastic block model, time-varying SBM, evolving block model | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| 관련≠ | 4 | 7 |
| 요약≠ | 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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