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OTARIOS: OpTimizing Author Ranking with Insiders/Outsiders Subnetworks

  • Jorge Silva
  • David Aparício
  • Fernando Silva
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)

Abstract

Evaluating scientists based on their scientific production is often a controversial topic. Nevertheless, bibliometrics and algorithmic approaches can assist traditional peer review in numerous tasks, such as attributing research grants, deciding scientific committees, or choosing faculty promotions. Traditional bibliometrics focus on individual measures, disregarding the whole data (i.e., the whole network). Here we put forward OTARIOS, a graph-ranking method which combines multiple publication/citation criteria to rank authors. OTARIOS divides the original network in two subnetworks, insiders and outsiders, which is an adequate representation of citation networks with missing information. We evaluate OTARIOS on a set of five real networks, each with publications in distinct areas of Computer Science. When matching a metric’s produced ranking with best papers awards received, we observe that OTARIOS is \({>} 20\%\) more accurate than traditional bibliometrics. We obtain the best results when OTARIOS considers (i) the author’s publication volume and publication recency, (ii) how recently his work is being cited by outsiders, and (iii) how recently his work is being cited by insiders and how individual he his.

Notes

Acknowledgments

This work is partially funded by the ERDF through the COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT as part of project UID/EEA/50014/2013. Jorge Silva is supported by a FCT/MAP-i PhD grant (PD/BD/128157/2016). David Aparício is also supported by a FCT/MAP-i PhD grant (PD/BD/105801/2014).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.CRACS & INESC-TEC, DCC-FCUPUniversidade do PortoPortoPortugal

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