Compară metode

	Detecția Comunităților Temporale	Detecția ponderată a comunităților
Domeniu	Analiza rețelelor	Analiza rețelelor
Familie	Machine learning	Machine learning
Anul apariției	2010	2004–2008
Autorul original	Mucha, P. J. et al.	Newman, M. E. J.; Blondel et al.
Tip	Network clustering algorithm	Graph clustering / community detection
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.	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.
Denumiri alternative	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
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.	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.