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| Gewichtete Gemeinschaftserkennung× | Modularitätsanalyse× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2004–2008 | 2004 |
| Urheber≠ | Newman, M. E. J.; Blondel et al. | Newman, M. E. J. & Girvan, M. |
| Typ≠ | Graph clustering / community detection | Community detection / graph partitioning |
| Wegweisende Quelle≠ | Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| Aliasnamen | weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | Weighted community detection identifies densely connected groups — communities — in networks where edges carry numeric strengths (weights). By incorporating edge weights into the modularity function, it reveals structure that binary adjacency alone would miss: two nodes connected by a strong tie are treated as more similar than two nodes linked by a weak one. The Louvain algorithm is the dominant practical implementation. | 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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