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| Súlyozott közelségi központiság× | Eigenvektor-központiság× | |
|---|---|---|
| Tudományterület | Hálózatelemzés | Hálózatelemzés |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 2010 | 1972 |
| Megalkotó≠ | Opsahl, T.; Agneessens, F.; Skvoretz, J. | Bonacich, P. |
| Típus≠ | Centrality measure (network analysis) | Centrality measure |
| Alapmű≠ | Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI ↗ | Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI ↗ |
| Alternatív nevek | weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness | eigenvector centrality, EC, Bonacich centrality, power centrality |
| Kapcsolódó | 6 | 6 |
| Összefoglaló≠ | Weighted closeness centrality extends the classic closeness measure to networks where edges carry numerical weights — such as frequency, strength, or cost — by incorporating those weights into shortest-path distances. Nodes that can reach others quickly along strong or efficient connections receive higher scores, making it a richer indicator of information-spreading potential than its binary counterpart. | 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. |
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