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| Gradzentralität× | Betweenness-Zentralität× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1978 | 1977 |
| Urheber | Freeman, L. C. | Freeman, L. C. |
| Typ≠ | Node-level centrality measure | Centrality measure |
| Wegweisende Quelle≠ | Freeman, L. C. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Aliasnamen | node degree, degree score, DC, connectivity centrality | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | 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. | Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes. |
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