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| Rilevamento di Comunità Multistrato× | Modello a Blocchi Stocastici× | |
|---|---|---|
| Campo | Analisi delle reti | Analisi delle reti |
| Famiglia≠ | Machine learning | Process / pipeline |
| Anno di origine≠ | 2010–2014 | 1983 |
| Ideatore≠ | Mucha, P. J. et al.; Kivela, M. et al. | — |
| Tipo≠ | Community detection algorithm for multilayer networks | Probabilistic generative graph model |
| Fonte seminale≠ | Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271. DOI ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| Alias | multilayer clustering, multiplex community detection, cross-layer community detection, MCD | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| Correlati≠ | 5 | 7 |
| Sintesi≠ | Multilayer community detection identifies groups of nodes that are densely connected across multiple types of relationships simultaneously. By coupling layers of a network — such as friendship, advice, and collaboration ties — it finds communities that are coherent not just within one relation type but across all of them, revealing structure that single-layer analysis would miss. | 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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