Bandingkan kaedah

	Analisis Rangkaian Multiplex Temporal	Pengesanan Komuniti Dinamik
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Machine learning
Tahun asal	2012–2014	2010 (key formalization); earlier work 2002–2009
Pengasas	Kivela, M.; Holme, P.; Saramaki, J. (among foundational contributors)	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)
Jenis	Structural and dynamic network analysis	Graph clustering / community discovery
Sumber perintis	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.	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.
Alias	TMNA, time-varying multiplex network analysis, dynamic multiplex network analysis, temporal multilayer network analysis	DCD, temporal community detection, evolving community detection, dynamic graph clustering
Berkaitan	5	5
Ringkasan	Temporal multiplex network analysis studies relational systems in which actors are connected by multiple distinct types of relationships that all evolve over time. By simultaneously tracking layer heterogeneity and temporal dynamics, the method reveals how different interaction channels co-evolve, which actors hold persistent cross-layer influence, and how structural changes propagate across relationship types and time periods.	Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research.