Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Gerichte Multiplexnetwerkanalyse× | Gerichte Betweenness Centraliteit× | |
|---|---|---|
| Vakgebied | Netwerkanalyse | Netwerkanalyse |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2013–2014 | 1977 |
| Grondlegger≠ | Kivela, M.; De Domenico, M. et al. | Freeman, L. C. |
| Type≠ | Multi-layer directed graph framework | Centrality measure (directed graph) |
| Oorspronkelijke bron≠ | 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 ↗ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Aliassen | directed multilayer network analysis, directed multiplex graphs, asymmetric multiplex network analysis, DMNA | directed BC, digraph betweenness, asymmetric betweenness centrality, directed Freeman betweenness |
| Verwant≠ | 6 | 5 |
| Samenvatting≠ | 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. | Directed Betweenness Centrality extends Freeman's classic betweenness measure to directed graphs, quantifying how often a node lies on the shortest directed paths between all other pairs of nodes. It identifies gatekeepers, brokers, and bottlenecks in asymmetric flows such as information cascades, citation networks, and organizational hierarchies. |
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