Abstract
Diffusion of innovations and knowledge is in most cases accounted for by the logistic model. Fieldwork research however constantly report that empirical data utterly deviate from this mathematical function. This chapter scrutinizes network forcing of diffusion process. The departure of empirical data from the logistic function is explained by social network discreteness, heterogeneity and anisotropy. New indices are proposed. Results arc illustrated by empirical data from an original study of knowledge diffusion in the medieval academic network.
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Raynaud, D. (2010). Why Do Diffusion Data Not Fit the Logistic Model? A Note on Network Discreteness, Heterogeneity and Anisotropy. In: Memon, N., Alhajj, R. (eds) From Sociology to Computing in Social Networks. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0294-7_12
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