Abstract
In this paper, we suggest a model of preferential attachment in coauthorship social networks. The process of one actor attaching to another actor (author) and strengthening the tie over time is a stochastic random process based on the distributions of tie-strength and clique size among actors. We will use empirical data to obtain the distributions. The proposed model will be utilized to predict emerging scientific subfields by observing the evolution of the coauthorship network over time. Further, we will examine the distribution of tie-strength of some prominent scholars to investigate the style of coauthorship. Finally, we present an example of a simulated coauthorship network generated randomly to compare with a real-world network.
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Acknowledgements
The work of Dr. Said is supported in part by Grant Number F32AA015876 from the National Institute on Alcohol Abuse and Alcoholism. The work of Dr. Wegman is supported in part by the Army Research Office under contract W911NF-04-1-0447. Both were also supported in part by the Army Research Laboratory under contract W911NF-07-1-0059. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Alcohol Abuse and Alcoholism or the National Institutes of Health.
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Said, Y.H., Wegman, E.J., Sharabati, W.K. (2010). Author–Coauthor Social Networks and Emerging Scientific Subfields. In: Palumbo, F., Lauro, C., Greenacre, M. (eds) Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03739-9_30
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DOI: https://doi.org/10.1007/978-3-642-03739-9_30
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