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| Dünaamiline eigenvector-kesksus× | Ajaline kogukonnatuvastus× | |
|---|---|---|
| Valdkond | Võrgustikuanalüüs | Võrgustikuanalüüs |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2010s | 2010 |
| Looja≠ | Lerman, K.; Ghosh, R.; Kang, J. H. | Mucha, P. J. et al. |
| Tüüp≠ | Centrality measure for time-evolving networks | Network clustering algorithm |
| Algallikas≠ | Lerman, K., Ghosh, R., & Kang, J. H. (2010). Centrality metric for dynamic networks. Proceedings of the 8th Workshop on Mining and Learning with Graphs (MLG '10). ACM. link ↗ | 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 ↗ |
| Rööpnimetused | temporal eigenvector centrality, time-varying eigenvector centrality, dynamic EC, evolving eigenvector centrality | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection |
| Seotud≠ | 4 | 6 |
| Kokkuvõte≠ | Dynamic eigenvector centrality extends the classic eigenvector centrality measure to networks that change over time. Rather than computing a single leading eigenvector on a static adjacency matrix, it tracks how a node's influence — defined by the importance of its neighbours — evolves across snapshots or time windows. The method is used in social network analysis, epidemiology, and information diffusion studies where network topology shifts continuously. | 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. |
| ScholarGateAndmestik ↗ |
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