Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Centralidad de Cercanía Ponderada× | Centralidad del vector propio× | |
|---|---|---|
| Campo | Análisis de redes | Análisis de redes |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2010 | 1972 |
| Autor original≠ | Opsahl, T.; Agneessens, F.; Skvoretz, J. | Bonacich, P. |
| Tipo≠ | Centrality measure (network analysis) | Centrality measure |
| Fuente seminal≠ | 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 ↗ |
| Alias | weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness | eigenvector centrality, EC, Bonacich centrality, power centrality |
| Relacionados | 6 | 6 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
|
|