Compară metode

	Detecția Comunităților Temporale	Detecția dirijată a comunităților
Domeniu	Analiza rețelelor	Analiza rețelelor
Familie	Machine learning	Machine learning
Anul apariției	2010	2008
Autorul original	Mucha, P. J. et al.	Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.
Tip	Network clustering algorithm	Graph partitioning / modularity optimization
Sursa seminală	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.
Denumiri alternative	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
Înrudite	6	6
Rezumat	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.