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Dinamičko otkrivanje zajednica×Откривање вишеслојних заједница×
OblastAnaliza mrežaAnaliza mreža
PorodicaMachine learningMachine learning
Godina nastanka2010 (key formalization); earlier work 2002–20092010–2014
TvoracMucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)Mucha, P. J. et al.; Kivela, M. et al.
TipGraph clustering / community discoveryCommunity detection algorithm for multilayer networks
Temeljni izvorMucha, 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 ↗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 ↗
Drugi naziviDCD, temporal community detection, evolving community detection, dynamic graph clusteringmultilayer clustering, multiplex community detection, cross-layer community detection, MCD
Srodne55
SažetakDynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research.Multilayer community detection identifies groups of nodes that are densely connected across multiple types of relationships simultaneously. By coupling layers of a network — such as friendship, advice, and collaboration ties — it finds communities that are coherent not just within one relation type but across all of them, revealing structure that single-layer analysis would miss.
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ScholarGateUporedite metode: Dynamic Community Detection · Multilayer Community Detection. Preuzeto 2026-06-17 sa https://scholargate.app/sr/compare