Σύγκριση μεθόδων

	Ανάλυση Διάχυσης σε Χρονικά Δίκτυα	Ανάλυση Διάχυσης Δικτύου
Πεδίο	Ανάλυση Δικτύων	Ανάλυση Δικτύων
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2012	1927 (epidemic roots); network formalization 1990s–2000s
Δημιουργός	Holme, P. & Saramäki, J.	Kermack, W. O. & McKendrick, A. G.
Τύπος	Network analysis framework	Simulation / analytical model
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks	diffusion on networks, information diffusion, contagion spreading model, network propagation model
Συναφείς	5	5
Σύνοψη	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.