Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Dinamiskais nejaušo grafu modelis (TERGM / STERGM)× | Stohastiskais bloku modelis× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime≠ | Machine learning | Process / pipeline |
| Izcelsmes gads≠ | 2010–2014 | 1983 |
| Autors≠ | Hanneke, Fu & Xing; Krivitsky & Handcock | — |
| Tips≠ | Probabilistic graphical model (temporal) | Probabilistic generative graph model |
| Pirmavots≠ | Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605. DOI ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| Citi nosaukumi | TERGM, Temporal ERGM, Dynamic ERGM, STERGM | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| Saistītās≠ | 4 | 7 |
| Kopsavilkums≠ | The Dynamic Exponential Random Graph Model (TERGM / STERGM) extends the classic ERGM framework to panel network data, modeling how a network's ties form and dissolve over time as a function of structural tendencies, nodal attributes, and the network's own past state. It provides statistically principled inference about longitudinal network change. | 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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