Linganisha mbinu

	Uchambuzi wa Modularityo wa Kina	Uchanganuzi wa Modularity
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2010	2004
Mwanzilishi	Mucha, P. J.; Porter, M. A.; and colleagues	Newman, M. E. J. & Girvan, M.
Aina	Community detection on temporal networks	Community detection / graph partitioning
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.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Majina mbadala	dynamic community structure analysis, temporal modularity optimization, evolving community detection, time-varying modularity	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Zinazohusiana	5	5
Muhtasari	Dynamic modularity analysis extends the classical modularity framework to networks that evolve over time, detecting communities across a sequence of network snapshots while penalizing unnecessary community changes between time steps. It identifies cohesive groups and tracks how they form, merge, split, or dissolve, giving researchers a principled view of structural change in longitudinal network data.	Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.