Bandingkan metode

	Betweenness Centrality Temporal	Analisis Difusi Jaringan Temporal
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2012	2012
Pencetus	Kim, H. & Anderson, R.; Holme, P. & Saramäki, J.	Holme, P. & Saramäki, J.
Tipe	Centrality measure for temporal networks	Network analysis framework
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	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks
Terkait	6	5
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 Network Diffusion Analysis studies how information, disease, influence, or other contagions spread through networks whose structure changes over time. By modeling edges as time-stamped contacts rather than static links, it captures the critical role of timing and ordering in determining which nodes get reached, how fast, and through which pathways — producing conclusions that static network models systematically miss.