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| Gewichtete Netzwerkanalyse (Weighted Social Network Analysis)× | Modularitätsanalyse× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2004–2010 | 2004 |
| Urheber≠ | Barrat, A.; Opsahl, T. et al. | Newman, M. E. J. & Girvan, M. |
| Typ≠ | Network analysis framework | Community detection / graph partitioning |
| Wegweisende Quelle≠ | Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| Aliasnamen | Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | Weighted Social Network Analysis extends classical SNA by assigning numeric values — weights — to ties between actors, capturing tie strength, interaction frequency, or resource flow. Rather than treating all connections as equal, it reveals who holds privileged positions by virtue of the intensity, not merely the existence, of their relationships. | 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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