Abstract
The citation-based measure is known unpredictable. However, it is used quite often in the cases when quantitatively evaluating the academic impact is required. With the development of social networks, it is natural to ask the question: is there any trustworthy model which is able to provide quantitatively analysis of the academic impact with a huge amount of relevant information instead of peer-review only before the prevalence of social media? Many efforts have been devoted to provide the standard academic evaluation indicators, but they are either inadequate to be fully qualified or unable to become the universal applicable measure. In this paper, we propose a systematic approach, named Attenuation Mechanism, to quantitatively analysis the academic evaluation based on four estimated factors. It would bring new insights into how the academic impact takes place and the influence it has (either short term or long term). The extensive experiments on real academic search datasets show that the proposed model can perform significantly better than the baseline models in different areas and different disciplines.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Garfield, E.: The history and meaning of the journal impact factor. JAMA J. Am. Med. Assoc. 295(1), 90–93 (2006)
Price, D.J.: Networks of scientific papers. Science 149(3683), 510–515 (1965)
Redner, S.: Citation statistics from 110 years of physical review. Phys. Today 58(6), 49–54 (2005)
Hirsch, J.E.: An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. USA 102(46), 16569–16572 (2005)
Jones, B.F., Wuchty, S., Uzzi, B.: Multi-university research teams: shifting impact, geography, and stratification in science. Science 322(5905), 1259–1262 (2008)
Lehmann, S., Jackson, A.D., Lautrup, B.E.: Measures for measures. Nature 444(7122), 1003–1004 (2006)
Phan, X.H., Nguyen, L.M., Horiguchi, S.: Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In: Proceedings of the 17th international conference on World Wide Web, pp. 91–100. ACM (2008)
Radicchi, F., Fortunato, S., Castellano, C.: Universality of citation distributions: toward an objective measure of scientific impact. Proc. Natl. Acad. Sci. USA 105(45), 17268–17272 (2008)
Barabsi, A.L., Song, C., Wang, D.: Publishing: handful of papers dominates citation. Nature 491(7422) (2012)
Evans, J.A., Foster, J.G.: Metaknowledge. Science 331(6018), 721–725 (2011)
Evans, J.A., Reimer, J.: Open access and global participation in science. Science 323(5917), 1025–1025 (2009)
Egghe, L.: Theory and practice of the g-index. Scientometrics 69(1), 131–152 (2006)
Fersht, A.: The most influential journals: impact factor and eigenfactor. Proc. Natl. Acad. Sci. USA 106(17), 6883–6884 (2009)
Wang, D., Song, C., Barabsi, A.L.: Quantifying long-term scientific impact. Science 342(6154), 127–132 (2013)
Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Book, G.J.: Book review: scale-free networks by G. Caldarelli. Int. J. Microstruct. Mater. Prop. 4(4), 520–521 (2009)
Medo, M., Cimini, G., Gualdi, S.: Temporal effects in the growth of networks. Phys. Rev. Lett. 107(23), 1261–1267 (2011)
Barabsi, A.: Evolution of networks: from biological nets to the Internet and WWW. Eur. J. Phys. 25(5), 697 (2010)
Bianconi, G., Barabsi, A.L.: Competition and multiscaling in evolving networks. Physics 30(1), 37–43 (2000)
Caldarelli, G., Capocci, A., De, L.R.P., et al.: Scale-free networks from varying vertex intrinsic fitness. Phys. Rev. Lett. 89(25), 148–168 (2002)
Bass, F.M.: A new product growth for model consumer durables. Manag. Sci. 50(12 Supplement), 215–227 (2010)
Gompertz, B.: On the nature of the function expressive of the law of human mortality and on a new mode of determining life contingencies. Philos. Trans. Roy. Soc. Lond. 115, 513–585 (2013)
Acknowledgement
This work was supported by National Natural Science Foundations of China (No. 61170192).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Li, F., Yu, W., Zhang, J., Li, L. (2016). Quantitative Analysis Academic Evaluation Based on Attenuation-Mechanism. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-47650-6_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47649-0
Online ISBN: 978-3-319-47650-6
eBook Packages: Computer ScienceComputer Science (R0)