Salīdzināt metodes

	Temporālā kopienu noteikšana	Vērsta kopienu noteikšana
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2010	2008
Autors	Mucha, P. J. et al.	Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.
Tips	Network clustering algorithm	Graph partitioning / modularity optimization
Pirmavots	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.	Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.
Citi nosaukumi	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
Saistītās	6	6
Kopsavilkums	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.