Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse de modularité temporelle× | Analyse de modularité pondérée× | |
|---|---|---|
| Domaine | Analyse de réseaux | Analyse de réseaux |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2010 | 2004 |
| Auteur d'origine≠ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. | Newman, M. E. J. |
| Type≠ | Community detection (temporal extension of modularity optimization) | Community structure optimization on weighted graphs |
| Source fondatrice≠ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876-878. DOI ↗ | Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131. DOI ↗ |
| Alias | dynamic modularity, time-varying modularity, longitudinal community detection, temporal community structure analysis | weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity |
| Apparentées | 5 | 5 |
| Résumé≠ | Temporal modularity analysis extends standard modularity-based community detection to time-varying networks by treating each time slice as a network layer and coupling adjacent layers with inter-temporal links. This allows researchers to identify how communities form, persist, merge, split, and dissolve over time in dynamic relational data. | 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. |
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