Salīdzināt metodes

	Laika tīklu difūzijas analīze	Laika starpības centrālās vērtības noteikšana
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2012	2012
Autors	Holme, P. & Saramäki, J.	Kim, H. & Anderson, R.; Holme, P. & Saramäki, J.
Tips	Network analysis framework	Centrality measure for temporal networks
Pirmavots	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.
Citi nosaukumi	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks	TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness
Saistītās	5	6
Kopsavilkums	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.