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| Gewichtete Gemeinschaftserkennung× | Multiplex-Netzwerkanalyse× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2004–2008 | 2014 |
| Urheber≠ | Newman, M. E. J.; Blondel et al. | Kivela, M.; Boccaletti, S. et al. |
| Typ≠ | Graph clustering / community detection | Structural network model |
| 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 ↗ | Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271. DOI ↗ |
| Aliasnamen | weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD | multiplex networks, multi-layer network analysis, multilayer network analysis, MNA |
| Verwandt | 6 | 6 |
| 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. | Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities. |
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