Why Do Diffusion Data not Fit the Logistic Model? A Note on Network Discreteness, Heterogeneity & Anisotropy

Dominique Raynaud (1, 2)

1 PLC - Philosophie, Langages et Cognition
2 GEMAS - Groupe d'étude des méthodes de l'analyse sociologique

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 are illustrated by empirical data from an original study of knowledge diffusion in the medieval academic network.

Mots clés: Logistic; Diffusion; Irregularity; Network; Discreteness; Heterogeneity; Anisotropy

Domaines: Histoire, Philosophie et Sociologie des sciences

Dominique Raynaud. Why Do Diffusion Data not Fit the Logistic Model? A Note on Network Discreteness, Heterogeneity & Anisotropy. N. Memon and R. Alhajj, eds. From Sociology to Computing in Social Networks: Theory, Foundations and Applications, Springer-Verlag, pp.81-96, 2010. ⟨halshs-00482597⟩