Bandingkan metode

	Analisis Jaringan Temporal Berbobot	Analisis Difusi Jaringan
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2004–2012	1927 (epidemic roots); network formalization 1990s–2000s
Pencetus	Holme, P. & Saramaki, J. (temporal networks); Barrat et al. (weighted networks)	Kermack, W. O. & McKendrick, A. G.
Tipe	Network analysis technique	Simulation / analytical model
Sumber perintis	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Kermack, W. O. & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115(772), 700–721.
Alias	WTNA, weighted time-varying network analysis, weighted dynamic network analysis, weighted evolving network analysis	diffusion on networks, information diffusion, contagion spreading model, network propagation model
Terkait	6	5
Ringkasan	Weighted temporal network analysis studies networks whose edges carry numerical weights — representing interaction strength, frequency, or intensity — and whose structure changes over time. It combines the time-varying perspective of temporal network analysis with the quantitative precision of weighted graph metrics, revealing not only when connections exist but how strong they are at each moment.	Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out locally.