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| Ajaline kogukonnatuvastus× | Kaalutud kogukonna tuvastamine× | |
|---|---|---|
| Valdkond | Võrgustikuanalüüs | Võrgustikuanalüüs |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2010 | 2004–2008 |
| Looja≠ | Mucha, P. J. et al. | Newman, M. E. J.; Blondel et al. |
| Tüüp≠ | Network clustering algorithm | Graph clustering / community detection |
| Algallikas≠ | 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 ↗ | 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 ↗ |
| Rööpnimetused | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection | weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD |
| Seotud | 6 | 6 |
| Kokkuvõte≠ | 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. | 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. |
| ScholarGateAndmestik ↗ |
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