Comparer des méthodes

	Modèles de diffusion en réseau	Analyse des réseaux temporels
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Process / pipeline	Process / pipeline
Année d'origine	1927 (epidemiological compartmental); 2003 (social influence cascade)	2012
Auteur d'origine	Kermack & McKendrick (SIR/SIS, 1927); Kempe, Kleinberg & Tardos (Independent Cascade, 2003)	Holme & Saramäki (2012) — seminal framework
Type	Stochastic / deterministic simulation on graphs	Dynamic graph analysis
Source fondatrice	Kermack, W.O. & McKendrick, A.G. (1927). A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London. Series A, 115(772), 700-721.	Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.
Alias	epidemic spreading models, compartmental models, influence propagation models, Ağ Yayılım Modelleri (SIR, SIS, Independent Cascade)	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
Apparentées	5	3
Résumé	Network diffusion models are a family of compartmental and probabilistic frameworks that simulate how information, disease, or innovation spreads across a connected system. Rooted in the mathematical epidemiology of Kermack and McKendrick (1927), the SIR and SIS models partition nodes into states and track transitions driven by contact rates and recovery probabilities. The Independent Cascade and Linear Threshold models, formalised by Kempe, Kleinberg, and Tardos (2003), extend this logic to social influence, modelling how activation propagates through a network one neighbour at a time.	Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system.