Bandingkan metode

	Betweenness Centrality Temporal	Sentralitas Derajat Temporal
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2012	2011–2012
Pencetus	Kim, H. & Anderson, R.; Holme, P. & Saramäki, J.	Holme, P.; Saramaki, J.; Kim, H.; Anderson, R.
Tipe	Centrality measure for temporal networks	Centrality measure (temporal extension)
Sumber perintis	Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Alias	TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness	time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC
Terkait	6	6
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 degree centrality extends the classic degree centrality to time-varying networks by counting how many distinct contacts a node accumulates over time. Rather than collapsing a dynamic network into a single static graph, it preserves the temporal order of edges, yielding a more faithful measure of a node's activity and reachability across the observation window.