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| Eigenvektor-központiság× | A node szerepének mérése a hálózatban: Köztes szerep (Betweenness Centrality)× | |
|---|---|---|
| Tudományterület | Hálózatelemzés | Hálózatelemzés |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 1972 | 1977 |
| Megalkotó≠ | Bonacich, P. | Freeman, L. C. |
| Típus | Centrality measure | Centrality measure |
| Alapmű≠ | 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. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Alternatív nevek | eigenvector centrality, EC, Bonacich centrality, power centrality | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| Kapcsolódó | 6 | 6 |
| Összefoglaló≠ | 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. | 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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