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| Mô hình Khối Ngẫu nhiên Theo Thời gian× | Phân tích Tính Mô-đun Theo Thời gian× | |
|---|---|---|
| Lĩnh vực | Phân tích mạng lưới | Phân tích mạng lưới |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2014–2017 | 2010 |
| Người khởi xướng≠ | Xu, K. S. & Hero, A. O.; Matias, C. & Miele, V. | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. |
| Loại≠ | Generative probabilistic model | Community detection (temporal extension of modularity optimization) |
| 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 ↗ | 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 ↗ |
| Tên gọi khác | TSBM, dynamic stochastic block model, time-varying SBM, evolving block model | dynamic modularity, time-varying modularity, longitudinal community detection, temporal community structure analysis |
| Liên quan≠ | 4 | 5 |
| 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. | Temporal modularity analysis extends standard modularity-based community detection to time-varying networks by treating each time slice as a network layer and coupling adjacent layers with inter-temporal links. This allows researchers to identify how communities form, persist, merge, split, and dissolve over time in dynamic relational data. |
| ScholarGateBộ dữ liệu ↗ |
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