Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mfumo wa Kuzuia Kazi nyingi wa Kimahesabu× | Ugunduzi wa Jumuiya za Tabaka Nyingi× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Mitandao | Uchanganuzi wa Mitandao |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2015-2017 | 2010–2014 |
| Mwanzilishi≠ | Peixoto, T. P.; De Bacco, C. and colleagues | Mucha, P. J. et al.; Kivela, M. et al. |
| Aina≠ | Generative probabilistic model | Community detection algorithm for multilayer networks |
| Chanzo asilia≠ | Peixoto, T. P. (2015). Inferring the mesoscale structure of layered, edge-valued, and time-varying networks. Physical Review E, 92(4), 042807. DOI ↗ | 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 ↗ |
| Majina mbadala | ML-SBM, multilayer SBM, multi-layer stochastic block model, multiplex stochastic block model | multilayer clustering, multiplex community detection, cross-layer community detection, MCD |
| Zinazohusiana≠ | 4 | 5 |
| Muhtasari≠ | The Multilayer Stochastic Block Model (ML-SBM) is a generative probabilistic framework that extends the classical stochastic block model to networks with multiple relation types or layers. It simultaneously infers community structure and block-to-block connection probabilities across all layers, capturing how communities cohere differently depending on context or relationship type. | 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. |
| ScholarGateSeti ya data ↗ |
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