Bandingkan kaedah

	Analisis Rangkaian Temporal Berbobot	Analisis Jaringan Sosial Berbobot
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Machine learning
Tahun asal	2004–2012	2004–2010
Pengasas	Holme, P. & Saramaki, J. (temporal networks); Barrat et al. (weighted networks)	Barrat, A.; Opsahl, T. et al.
Jenis	Network analysis technique	Network analysis framework
Sumber perintis	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Barrat, A., Barthélemy, 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	Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis
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 Social Network Analysis extends classical SNA by assigning numeric values — weights — to ties between actors, capturing tie strength, interaction frequency, or resource flow. Rather than treating all connections as equal, it reveals who holds privileged positions by virtue of the intensity, not merely the existence, of their relationships.