Bandingkan kaedah

	Kemeradulan Rentasan Masa	Analisis Jaringan Sosial Temporal
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Machine learning
Tahun asal	2012	2000s–2010s
Pengasas	Kim, H. & Anderson, R.; Holme, P. & Saramäki, J.	Moody, J.; Holme, P.; Saramäki, J.
Jenis	Centrality measure for temporal networks	Longitudinal network analysis
Sumber perintis	Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Alias	TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness	TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA
Berkaitan	6	4
Ringkasan	Temporal Betweenness Centrality (TBC) extends classical betweenness centrality to time-stamped networks by counting how often a node lies on time-respecting shortest paths — paths that traverse edges in chronological order. It identifies nodes that act as temporal brokers, controlling information or resource flow as it evolves over time, rather than in a static snapshot.	Temporal Social Network Analysis (TSNA) extends classic social network analysis by treating networks as time-varying structures. Rather than aggregating all ties into a single static snapshot, TSNA tracks when ties form, persist, and dissolve, enabling researchers to study how social structures evolve and how dynamic connectivity shapes diffusion, influence, and inequality over time.