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| Rilevamento di Comunità Temporali× | Rilevamento di Comunità Dirette× | |
|---|---|---|
| Campo | Analisi delle reti | Analisi delle reti |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2010 | 2008 |
| Ideatore≠ | Mucha, P. J. et al. | Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T. |
| Tipo≠ | Network clustering algorithm | Graph partitioning / modularity optimization |
| Fonte seminale≠ | 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 ↗ | Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703. DOI ↗ |
| Alias | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection | directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning |
| Correlati | 6 | 6 |
| Sintesi≠ | 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. | Directed community detection identifies densely interconnected groups of nodes in a directed network, accounting for the asymmetry of edges (e.g., A follows B does not imply B follows A). Adapting modularity or flow-based criteria to directed graphs reveals clusters that undirected methods systematically miss, making it essential for citation networks, follower graphs, and biological regulatory pathways. |
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