השוואת שיטות

	ניתוח מודולריות דינמי	ניתוח רשתות מרובות (Multiplex Network Analysis)
תחום	ניתוח רשתות	ניתוח רשתות
משפחה	Machine learning	Machine learning
שנת המקור	2010	2014
הוגה השיטה	Mucha, P. J.; Porter, M. A.; and colleagues	Kivela, M.; Boccaletti, S. et al.
סוג	Community detection on temporal networks	Structural network model
מקור מכונן	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.	Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.
כינויים	dynamic community structure analysis, temporal modularity optimization, evolving community detection, time-varying modularity	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
קשורות	5	6
תקציר	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.	Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities.