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Reference Details
Appoloni, Andrea, Swarup, Samarth and Fagyal, Zsuzsanna (2011), "Degree-biased Voter Model with Innovation: Modeling Lexical Inflation", Proceeding of AAMAS2011.

We analyze and extend a recently proposed model of linguistic di usion in social networks, to analytically derive time to convergence, and to account for the innovation phase of lexical dynamics in networks. Our new model, the degree-biased voter model with innovation, shows that the probability of existence of a norm is inversely related to innovation probability. When the innovation rate in the population is low, variants that become norms are due to a peripheral member with high probability. As the innovation rate increases, the fraction of time that the norm is a peripheral-introduced variant and the total time for which a norm exists at all in the population decrease. These results align with historical observations of rapid increase and generalization of slang words, technical terms, and new common expressions at times of cultural change in some languages.

Social Simulation, Lexical Innovation, Norms, Degree-biased Voter Model

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This article is 'Highly accessed'  (total accesses to this article since publication: 1197) relative to age  in  BioMed Central:
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As of August 27, the most downloaded/accessed paper in BMC Systems Biology is: D. van Dijk et al.: Identifying potential survival strategies of HIV-1 through virus-host protein interaction networks, BMC Systems Biology, vol. 4, nr 1 pp. 96+17. 2010.

This paper received the ICCS best paper award 2010:
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Procedia Computer Science vol. 1, nr 1 pp. 555-564. Elsevier B.V., Amsterdam, May 2010.

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EU Grant Agreement Number: 233847

BONUS Grant Number: OMFB-00237/2010

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