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Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Modelli di Diffusione di Rete× | Analisi delle Reti Temporali× | |
|---|---|---|
| Campo | Analisi delle reti | Analisi delle reti |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1927 (epidemiological compartmental); 2003 (social influence cascade) | 2012 |
| Ideatore≠ | Kermack & McKendrick (SIR/SIS, 1927); Kempe, Kleinberg & Tardos (Independent Cascade, 2003) | Holme & Saramäki (2012) — seminal framework |
| Tipo≠ | Stochastic / deterministic simulation on graphs | Dynamic graph analysis |
| Fonte seminale≠ | 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. DOI ↗ | Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125. DOI ↗ |
| 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) |
| Correlati≠ | 5 | 3 |
| Sintesi≠ | 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. |
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