Linganisha mbinu

	Uchambuzi wa Usambaaji wa Mitandao ya Muda	Uchanganuzi wa Uenezaji wa Mtandao
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2012	1927 (epidemic roots); network formalization 1990s–2000s
Mwanzilishi	Holme, P. & Saramäki, J.	Kermack, W. O. & McKendrick, A. G.
Aina	Network analysis framework	Simulation / analytical model
Chanzo asilia	Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Kermack, W. O. & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115(772), 700–721.
Majina mbadala	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks	diffusion on networks, information diffusion, contagion spreading model, network propagation model
Zinazohusiana	5	5
Muhtasari	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.	Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out locally.