Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Стохастична блокова модель× | Метод головних компонент× | |
|---|---|---|
| Галузь≠ | Мережевий аналіз | Машинне навчання |
| Родина≠ | Process / pipeline | Machine learning |
| Рік появи≠ | 1983 | 2002 |
| Автор методу≠ | — | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Тип≠ | Probabilistic generative graph model | Unsupervised dimensionality reduction |
| Основоположне джерело≠ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Інші назви | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Пов'язані≠ | 7 | 3 |
| Підсумок≠ | 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. | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. |
| ScholarGateНабір даних ↗ |
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