השוואת שיטות
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| מודל בלוקים סטוכסטי זמני× | ניתוח מודולריות זמנית× | |
|---|---|---|
| תחום | ניתוח רשתות | ניתוח רשתות |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 2014–2017 | 2010 |
| הוגה השיטה≠ | Xu, K. S. & Hero, A. O.; Matias, C. & Miele, V. | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. |
| סוג≠ | Generative probabilistic model | Community detection (temporal extension of modularity optimization) |
| מקור מכונן≠ | 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 ↗ |
| כינויים | TSBM, dynamic stochastic block model, time-varying SBM, evolving block model | dynamic modularity, time-varying modularity, longitudinal community detection, temporal community structure analysis |
| קשורות≠ | 4 | 5 |
| תקציר≠ | 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. |
| ScholarGateמערך נתונים ↗ |
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