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| Multilayer-Grad-Zentralität× | Gradzentralität× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2013–2014 | 1978 |
| Urheber≠ | Kivelä, M.; De Domenico, M. et al. | Freeman, L. C. |
| Typ≠ | Centrality measure for multilayer networks | Node-level centrality measure |
| Wegweisende Quelle≠ | Kivelä, 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. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| Aliasnamen | multilayer degree, multiplex degree centrality, overlapping-layer degree centrality, MDC | node degree, degree score, DC, connectivity centrality |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Multilayer degree centrality extends the classic degree centrality measure to networks composed of multiple layers — such as networks representing different types of social ties, communication channels, or relationship contexts simultaneously. It quantifies how many connections a node has across one or all layers, revealing nodes that are influential not just in a single context but across the entire multi-relational structure. | Degree centrality is the simplest and most intuitive measure of a node's importance in a network, defined as the number of direct ties a node has to other nodes. Normalized by dividing by the maximum possible ties, it allows comparison across networks of different sizes and is the starting point of almost every network analysis. |
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