Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Läheduskesksus× | Eigenvector Centrality× | |
|---|---|---|
| Valdkond | Võrgustikuanalüüs | Võrgustikuanalüüs |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 1950 (formalized 1979) | 1972 |
| Looja≠ | Bavelas, A.; formalized by Freeman, L. C. | Bonacich, P. |
| Tüüp≠ | Node-level centrality index | Centrality measure |
| Algallikas≠ | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ | Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI ↗ |
| Rööpnimetused | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality | eigenvector centrality, EC, Bonacich centrality, power centrality |
| Seotud | 6 | 6 |
| Kokkuvõte≠ | 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. | 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. |
| ScholarGateAndmestik ↗ |
|
|