เปรียบเทียบวิธี

	เซลลูลาร์ออโตมาตา	แบบจำลองการแพร่กระจายในเครือข่าย
สาขาวิชา	การจำลอง	การวิเคราะห์เครือข่าย
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1940s–1950s (formalized); 1970 (Conway's Game of Life); 2002 (Wolfram's systematic classification)	1927 (epidemiological compartmental); 2003 (social influence cascade)
ผู้ริเริ่ม	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)
ประเภท	Grid-based computational simulation model	Stochastic / deterministic simulation on graphs
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	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)
ที่เกี่ยวข้อง	5	5
สรุป	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.