Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Аналіз зваженої моділярності× | Зважена центральність за посередництвом× | |
|---|---|---|
| Галузь | Мережевий аналіз | Мережевий аналіз |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2004 | 2010 |
| Автор методу≠ | Newman, M. E. J. | Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001) |
| Тип≠ | Community structure optimization on weighted graphs | Centrality measure (path-based) |
| Основоположне джерело≠ | Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131. DOI ↗ | Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI ↗ |
| Інші назви | weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity | WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted) |
| Пов'язані≠ | 5 | 6 |
| Підсумок≠ | 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. | Weighted Betweenness Centrality extends Freeman's betweenness measure to edge-weighted graphs by routing shortest paths through a tunable transformation of edge weights. Nodes that sit on many high-value shortest paths receive high scores, identifying brokers and bridges in social, biological, and information networks where tie strength matters. |
| ScholarGateНабір даних ↗ |
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