Confronta i metodi

	Analisi di Diffusione su Reti Temporali	Centralità di intermediazione temporale
Campo	Analisi delle reti	Analisi delle reti
Famiglia	Machine learning	Machine learning
Anno di origine	2012	2012
Ideatore	Holme, P. & Saramäki, J.	Kim, H. & Anderson, R.; Holme, P. & Saramäki, J.
Tipo	Network analysis framework	Centrality measure for temporal networks
Fonte seminale	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	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks	TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness
Correlati	5	6
Sintesi	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.	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.