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| Temporale Zwei-Moden-Netzwerkanalyse× | Zeitliche Gemeinschaftserkennung× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1990s–2010s | 2010 |
| Urheber≠ | Borgatti, S. P. & Everett, M. G. (two-mode foundations); extended to temporal setting by multiple authors | Mucha, P. J. et al. |
| Typ≠ | Network analysis technique | Network clustering algorithm |
| Wegweisende Quelle≠ | Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269. DOI ↗ | 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 ↗ |
| Aliasnamen | temporal bipartite network analysis, dynamic two-mode network analysis, time-varying bipartite network analysis, longitudinal affiliation network analysis | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection |
| Verwandt≠ | 5 | 6 |
| Zusammenfassung≠ | Temporal two-mode network analysis tracks relationships between two distinct classes of nodes — such as authors and publications, or actors and events — across multiple time points. By combining bipartite structure with longitudinal observation, it reveals how affiliation patterns, collaborations, and community memberships form, evolve, and dissolve over time. | Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution. |
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