Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis de Modularidad Ponderada× | Centralidad de intermediación× | |
|---|---|---|
| Campo | Análisis de redes | Análisis de redes |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2004 | 1977 |
| Autor original≠ | Newman, M. E. J. | Freeman, L. C. |
| Tipo≠ | Community structure optimization on weighted graphs | Centrality measure |
| Fuente seminal≠ | Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131. DOI ↗ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Alias | weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| Relacionados≠ | 5 | 6 |
| Resumen≠ | Weighted modularity analysis extends the classical Newman-Girvan modularity measure to networks where edges carry numeric strengths (frequencies, intensities, costs). By replacing binary adjacency with tie weights, it finds community partitions that reflect how densely interconnected subgroups are relative to what is expected under a weighted null model, yielding more nuanced groupings than unweighted approaches on data where edge strength varies meaningfully. | 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. |
| ScholarGateConjunto de datos ↗ |
|
|