Σύγκριση μεθόδων

	Ανάλυση Διάχυσης σε Σταθμισμένα Δίκτυα	Ανάλυση Διάχυσης Δικτύου
Πεδίο	Ανάλυση Δικτύων	Ανάλυση Δικτύων
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2004	1927 (epidemic roots); network formalization 1990s–2000s
Δημιουργός	Barrat, A.; Newman, M. E. J.	Kermack, W. O. & McKendrick, A. G.
Τύπος	Network diffusion model	Simulation / analytical model
Θεμελιώδης πηγή	Barrat, A., Barthelemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.	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	WNDA, weighted diffusion process, edge-weighted spreading analysis, weighted information diffusion	diffusion on networks, information diffusion, contagion spreading model, network propagation model
Συναφείς	6	5
Σύνοψη	Weighted Network Diffusion Analysis models how information, influence, disease, or resources spread through a network whose edges carry quantitative strength values. By letting tie weights govern transition probabilities, the method produces more realistic spreading dynamics than binary-edge diffusion, revealing which high-traffic pathways dominate propagation in social, biological, and information networks.	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.