A Bayesian approach for the reliability of scientific co-authorship networks with emphasis on nodes |
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| Institution: | 1. Universidade Estadual Paulista, 17602-496 Tupã, SP, Brazil;2. Universidade de São Paulo, 13560-970 São Carlos, SP, Brazil;1. Institute for Criminal Law & Criminology, Leiden Law School, Leiden University, Leiden, The Netherlands;2. Research and Documentation Centre, Ministry of Security & Justice, The Hague, The Netherlands;3. VU University Amsterdam, Amsterdam, The Netherlands;4. Criminal Justice Department and the Criminal Justice PhD Program at John Jay College, City University of New York, United States;5. School of Criminal Justice at Michigan State University, East Lansing, United States;1. Headquarters, Department of the Army, United States Army, United States;2. Department of Statistics, George Mason University, United States;1. Institute for Quantitative Business and Economics Research (QBER), University of Kiel, Heinrich-Hecht-Platz 9, 24118 Kiel, Germany;2. Department of Economics, University of Kiel, Olshausenstr. 40, 24118 Kiel, Germany;3. Banco de España Chair in Computational Economics, University Jaume I, Campus del Riu Sec, 12071 Castellon, Spain;1. Department of Health Promotion & Behavioral Sciences, School of Public Health, The University of Texas Health Science Center at Houston, 7000 Fannin Street, UCT 2514, Houston, TX 77030-5401, United States;2. Department of Sociology, University of Groningen, Grote Rozenstraat 31, 9712 TG Groningen, The Netherlands;3. Nuffield College, University of Oxford, UK;4. Institute for Prevention Research, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032-3628, United States;1. IESEG School of Management (LEM CNRS 9221), Lille/Paris, France;2. IESE Business School, University of Navarra, Barcelona, Spain. Supported by the European Research Council –Ref. ERC-2011-StG 283300-REACTOPS and by the Spanish Ministry of Economics and Competitiveness (Ministerio de Economía y Competitividad) – Ref. ECO2014-59998-P;3. University of Barcelona, Barcelona, Spain |
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| Abstract: | The co-authorship among members of a research group commonly can be represented by a (co-authorship) graph in which nodes represent the researchers that make up of this group and edges represent the connections between two agents (i.e., the co-authorship between these agents). Current study measures the reliability of networks by taking into consideration unreliable nodes (researchers) and perfectly reliable edges (co-authorship between two researchers). A Bayesian approach for the reliability of a network represented by the co-authorship among members of a real research group is proposed, obtaining Bayesian estimates and credibility intervals for the individual components (nodes or researchers) and the network. Weakly informative and non-informative prior distributions are assumed for those components and the posterior summaries are obtained by Monte Carlo-Markov Chain methods. The results show the relevance of an inferential approach for the reliability of scientific co-authorship network. The results also demonstrate that the contribution of each researcher is highly relevant for the maintenance of a research group. In addition, the Bayesian methodology was a feasible and easy computational implementation. |
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| Keywords: | Social networks Graph theory Research group Bayesian inference MCMC simulation methods |
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