Bandingkan kaedah

	Analisis Rangkaian Multiplex Temporal	Analisis Rangkaian Temporal
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Process / pipeline
Tahun asal	2012–2014	2012
Pengasas	Kivela, M.; Holme, P.; Saramaki, J. (among foundational contributors)	Holme & Saramäki (2012) — seminal framework
Jenis	Structural and dynamic network analysis	Dynamic graph analysis
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.	Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.
Alias	TMNA, time-varying multiplex network analysis, dynamic multiplex network analysis, temporal multilayer network analysis	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
Berkaitan	5	3
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.	Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system.