Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Centrālā pakāpe× | Tuvuma centralitāte× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1978 | 1950 (formalized 1979) |
| Autors≠ | Freeman, L. C. | Bavelas, A.; formalized by Freeman, L. C. |
| Tips≠ | Node-level centrality measure | Node-level centrality index |
| Pirmavots≠ | Freeman, L. C. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| Citi nosaukumi | node degree, degree score, DC, connectivity centrality | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | 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. | Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts. |
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