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| Gewichtete Modularitätsanalyse× | Modularitätsanalyse× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr | 2004 | 2004 |
| Urheber≠ | Newman, M. E. J. | Newman, M. E. J. & Girvan, M. |
| Typ≠ | Community structure optimization on weighted graphs | Community detection / graph partitioning |
| Wegweisende Quelle≠ | Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| Aliasnamen | weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. | Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks. |
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