Confronta i metodi
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| Automi Cellulari× | Modelli di Diffusione di Rete× | |
|---|---|---|
| Campo≠ | Simulazione | Analisi delle reti |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1940s–1950s (formalized); 1970 (Conway's Game of Life); 2002 (Wolfram's systematic classification) | 1927 (epidemiological compartmental); 2003 (social influence cascade) |
| Ideatore≠ | John von Neumann and Stanislaw Ulam (1940s–1950s); popularized by John Conway (1970) and Stephen Wolfram (1980s–2002) | Kermack & McKendrick (SIR/SIS, 1927); Kempe, Kleinberg & Tardos (Independent Cascade, 2003) |
| Tipo≠ | Grid-based computational simulation model | Stochastic / deterministic simulation on graphs |
| Fonte seminale≠ | Wolfram, S. (2002). A New Kind of Science. Wolfram Media. ISBN: 978-1579550080 | 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 ↗ |
| Alias | CA, Hücresel Otomat (Cellular Automata), lattice model, grid-based simulation | epidemic spreading models, compartmental models, influence propagation models, Ağ Yayılım Modelleri (SIR, SIS, Independent Cascade) |
| Correlati | 5 | 5 |
| Sintesi≠ | Cellular automata (CA) is a grid-based computational simulation model, first formalized by John von Neumann and Stanislaw Ulam in the 1940s–1950s and brought to wide attention by John Conway's Game of Life (1970) and Stephen Wolfram's systematic classification (2002), in which a lattice of cells — each holding a finite discrete state — evolves in discrete time steps according to local neighborhood interaction rules, causing complex global patterns to emerge from simple local specifications. | 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. |
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