Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Īpašvektoru centralitāte× | Tuvuma centralitāte× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1972 | 1950 (formalized 1979) |
| Autors≠ | Bonacich, P. | Bavelas, A.; formalized by Freeman, L. C. |
| Tips≠ | Centrality measure | Node-level centrality index |
| Pirmavots≠ | Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI ↗ | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| Citi nosaukumi | eigenvector centrality, EC, Bonacich centrality, power centrality | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network. | 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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