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| ניתוח רשתות מולטיפלקס מכוונות× | ניתוח רשתות מרובות (Multiplex Network Analysis)× | |
|---|---|---|
| תחום | ניתוח רשתות | ניתוח רשתות |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 2013–2014 | 2014 |
| הוגה השיטה≠ | Kivela, M.; De Domenico, M. et al. | Kivela, M.; Boccaletti, S. et al. |
| סוג≠ | Multi-layer directed graph framework | Structural network model |
| מקור מכונן | 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 ↗ |
| כינויים | directed multilayer network analysis, directed multiplex graphs, asymmetric multiplex network analysis, DMNA | multiplex networks, multi-layer network analysis, multilayer network analysis, MNA |
| קשורות | 6 | 6 |
| תקציר≠ | Directed multiplex network analysis models systems where the same set of nodes are connected by multiple types of directed (asymmetric) relationships across distinct layers — such as citation flows, information cascades, or authority hierarchies co-existing simultaneously. It extends multiplex network analysis by preserving both layer identity and edge directionality, enabling richer structural and dynamic insights. | Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities. |
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