手法を比較

	エゴネットワーク分析	ネットワーク拡散モデル
分野	ネットワーク分析	ネットワーク分析
系統	Process / pipeline	Process / pipeline
提唱年	1992 (Burt); foundational measurement formalised by Marsden 2002	1927 (epidemiological compartmental); 2003 (social influence cascade)
提唱者	Ronald S. Burt (structural holes framework); Peter V. Marsden (egocentric measures)	Kermack & McKendrick (SIR/SIS, 1927); Kempe, Kleinberg & Tardos (Independent Cascade, 2003)
種類	Descriptive / relational network analysis	Stochastic / deterministic simulation on graphs
原典	Burt, R.S. (1992). Structural Holes: The Social Structure of Competition. Harvard University Press. ISBN: 9780674843714	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.
別名	personal network analysis, egocentric network analysis, Ego Ağı Analizi (Personal Network Analysis)	epidemic spreading models, compartmental models, influence propagation models, Ağ Yayılım Modelleri (SIR, SIS, Independent Cascade)
関連	6	5
概要	Ego network analysis examines the personal network of a focal individual — the ego — by mapping their direct contacts (alters) and the ties those contacts share with one another. Formalised through Ronald Burt's structural holes framework (1992) and Marsden's egocentric measurement approach (2002), the method produces ego-level indicators such as network size, density, constraint, and brokerage role that reveal how each individual's social position shapes their access to information, resources, and influence.	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.