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Moneyball for Academics: Network Analysis for Predicting Research Impact

17 Pages Posted: 5 Jan 2014  

Dimitris Bertsimas

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Erik Brynjolfsson

Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER)

Shachar Reichman

Massachusetts Institute of Technology (MIT) - Sloan School of Management; Tel Aviv University - Faculty of Management

John M. Silberholz

Massachusetts Institute of Technology (MIT) - Operations Research Center

Date Written: January 4, 2014

Abstract

How are scholars ranked for promotion, tenure and honors? How can we improve the quantitative tools available for decision makers when making such decisions? Can we predict the academic impact of scholars and papers at early stages using quantitative tools?

Current academic decisions (hiring, tenure, prizes) are mostly very subjective. In the era of “Big Data,” a solid quantitative set of measurements should be used to support this decision process.

This paper presents a method for predicting the probability of a paper being in the most cited papers using only data available at the time of publication. We find that highly cited papers have different structural properties and that these centrality measures are associated with increased odds of being in the top percentile of citation count.

The paper also presents a method for predicting the future impact of researchers, using information available early in their careers. This model integrates information about changes in a young researcher’s role in the citation network and co-authorship network and demonstrates how this improves predictions of their future impact.

These results show that the use of quantitative methods can complement the qualitative decision-making process in academia and improve the prediction of academic impact.

Keywords: Citation analysis, Academic impact, Analytics, Networks

Suggested Citation

Bertsimas, Dimitris and Brynjolfsson, Erik and Reichman, Shachar and Silberholz, John M., Moneyball for Academics: Network Analysis for Predicting Research Impact (January 4, 2014). Available at SSRN: https://ssrn.com/abstract=2374581 or http://dx.doi.org/10.2139/ssrn.2374581

Dimitris Bertsimas

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

E53-359
Cambridge, MA 02142
United States
617-253-4223 (Phone)
617-258-7579 (Fax)

Erik Brynjolfsson

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

E53-313
Cambridge, MA 02142
United States
617-253-4319 (Phone)

HOME PAGE: http://digital.mit.edu/erik

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Shachar Reichman (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

77 Massachusetts Ave.
E62-412
Cambridge, MA 02142
United States

Tel Aviv University - Faculty of Management ( email )

P.O. Box 39010
Ramat Aviv, Tel Aviv, 69978
Israel

John Silberholz

Massachusetts Institute of Technology (MIT) - Operations Research Center ( email )

77 Massachusetts Avenue
Bldg. E 40-149
Cambridge, MA 02139
United States

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