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Mathematical modeling of complex contagion on clustered networks

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posted on 2018-02-16, 12:19 authored by David J.P. O'Sullivan, Gary J. O'Keeffe, Peter G. Fennell, James P. Gleeson
The spreading of behavior, such as the adoption of a new innovation, is influenced by the structure of social networks that interconnect the population. In the experiments of Centola [15], adoption of new behavior was shown to spread further and faster across clustered-lattice networks than across corresponding random networks. This implies that the “complex contagion” effects of social reinforcement are important in such diffusion, in contrast to “simple” contagion models of disease-spread which predict that epidemics would grow more efficiently on random networks than on clustered networks. To accurately model complex contagion on clustered networks remains a challenge because the usual assumptions (e.g., of mean-field theory) regarding tree-like networks are invalidated by the presence of triangles in the network; the triangles are, however, crucial to the social reinforcement mechanism, which posits an increased probability of a person adopting behavior that has been adopted by two or more neighbors. In this paper we modify the analytical approach that was introduced by Hébert-Dufresne et al. [19], to study disease-spread on clustered networks. We show how the approximation method can be adapted to a complex contagion model, and confirm the accuracy of the method with numerical simulations. The analytical results of the model enable us to quantify the level of social reinforcement that is required to observe—as in Centola's experiments—faster diffusion on clustered topologies than on random networks.

Funding

PI: MARK LEISING/CLEMSON UNIVERSITY U.S. INTEGRAL USERS GROUP CHAIR SUMMARY: TO SUPPORT MY WORK AND TRAVEL AS CHAIR OF THE U.S. INTEGRAL USERS GROUP (US-IUG). ORGANIZE AND ATTEND 2 US-LUG MEETINGS AT GODDARD SPACE FLIGHT CENTER WORK WITH THE PROJECT TO EN

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History

Publication

Frontiers in Physics;3:71

Publisher

Frontiers Media

Note

peer-reviewed

Other Funding information

SFI, IRC, European Commission through FET-Proactive project PLEXMATH

Language

English

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