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| 시간적 확률 블록 모델× | 시간적 커뮤니티 탐지× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2014–2017 | 2010 |
| 창시자≠ | Xu, K. S. & Hero, A. O.; Matias, C. & Miele, V. | Mucha, P. J. et al. |
| 유형≠ | Generative probabilistic model | Network clustering algorithm |
| 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 ↗ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878. DOI ↗ |
| 별칭 | TSBM, dynamic stochastic block model, time-varying SBM, evolving block model | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection |
| 관련≠ | 4 | 6 |
| 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. | Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution. |
| ScholarGate데이터셋 ↗ |
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