Bandingkan kaedah

	Analisis Rangkaian Temporal Berbobot	Analisis Penyebaran Rangkaian Berbobot
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Machine learning
Tahun asal	2004–2012	2004
Pengasas	Holme, P. & Saramaki, J. (temporal networks); Barrat et al. (weighted networks)	Barrat, A.; Newman, M. E. J.
Jenis	Network analysis technique	Network diffusion model
Sumber perintis	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Barrat, A., Barthelemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.
Alias	WTNA, weighted time-varying network analysis, weighted dynamic network analysis, weighted evolving network analysis	WNDA, weighted diffusion process, edge-weighted spreading analysis, weighted information diffusion
Berkaitan	6	6
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.	Weighted Network Diffusion Analysis models how information, influence, disease, or resources spread through a network whose edges carry quantitative strength values. By letting tie weights govern transition probabilities, the method produces more realistic spreading dynamics than binary-edge diffusion, revealing which high-traffic pathways dominate propagation in social, biological, and information networks.