Linganisha mbinu

	Ugunduzi wa Jumuiya za Muda	Hutambua Jumuiya zenye Uzito
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2010	2004–2008
Mwanzilishi	Mucha, P. J. et al.	Newman, M. E. J.; Blondel et al.
Aina	Network clustering algorithm	Graph clustering / community detection
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	6	6
Muhtasari	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.