Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchanganuzi wa Mitandao Mingi ya Muda× | Ugunduzi wa Jumuiya za Tabaka Nyingi× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Mitandao | Uchanganuzi wa Mitandao |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2012–2014 | 2010–2014 |
| Mwanzilishi≠ | Kivela, M. et al.; Holme, P. & Saramaki, J. | Mucha, P. J. et al.; Kivela, M. et al. |
| Aina≠ | Network analysis framework | Community detection algorithm for multilayer networks |
| Chanzo asilia | 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 ↗ | 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 | MTNA, temporal multilayer network analysis, time-varying multilayer network analysis, dynamic multilayer network analysis | multilayer clustering, multiplex community detection, cross-layer community detection, MCD |
| Zinazohusiana≠ | 4 | 5 |
| Muhtasari≠ | Multilayer temporal network analysis studies relational systems in which nodes interact through multiple distinct types of ties that all evolve over time. By modeling each relationship type as a separate layer and tracking how those layers change across time snapshots, the method reveals how cross-layer dynamics and temporal patterns jointly shape information flow, influence spread, and community structure. | 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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