Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Gericht model voor Willekeurige Grafen (Directed ERGM)× | Stochastic Block Model× | |
|---|---|---|
| Vakgebied | Netwerkanalyse | Netwerkanalyse |
| Familie≠ | Machine learning | Process / pipeline |
| Jaar van ontstaan≠ | 1986 (foundations); 2007 (modern directed ERGM formulation) | 1983 |
| Grondlegger≠ | Frank, O. & Strauss, D.; extended by Robins, Pattison, Kalish & Lusher | — |
| Type≠ | Statistical generative model for directed networks | Probabilistic generative graph model |
| Oorspronkelijke bron≠ | Robins, G., Pattison, P., Kalish, Y. & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173-191. DOI ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| Aliassen | Directed ERGM, p-star model (directed), directed p* model, directed Markov graph model | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| Verwant≠ | 4 | 7 |
| Samenvatting≠ | The Directed Exponential Random Graph Model (Directed ERGM) is a family of statistical models for directed networks that estimates the probability of observing a given directed graph as a function of structural configurations — such as reciprocity, transitive triads, and in-degree centralization — and node or dyad covariates, enabling principled inference about the social processes that generate directed ties. | 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. |
| ScholarGateGegevensset ↗ |
|
|