nach oben

Erschienen in:

2017 | OriginalPaper | Buchkapitel

Predicting the Evolution of Scientific Output

verfasst von : Antonia Gogoglou, Yannis Manolopoulos

Erschienen in: Computational Collective Intelligence

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Various efforts have been made to quantify scientific impact and identify the mechanisms that influence its future evolution. The first step is the identification of what constitutes scholarly impact and how it is measured. In this direction, various approaches focus on future citation count or h-index prediction at author or publication level, on fitting the distribution of citation accumulation or accurately identifying award winners, upcoming hot research topics or academic rising stars. A plethora of features have been contemplated as possible influential factors and assorted machine-learning methodologies have been adopted to ensure timely and accurate estimations. Here, we provide an overview of the field challenges, as well as a taxonomy of the existing approaches to identify the open issues that are yet to be addressed.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Automatic Deduction of Learners’ Profiling Rules Based on Behavioral Analysis

Nächstes Kapitel Enhanced Hybrid Component-Based Face Recognition

Acuna, D.E., Allesina, S., Kording, K.P.: Future impact: predicting scientific success. Nature 489(7415), 201–202 (2012)CrossRef

Börner, K., Dall’Asta, L., Ke, W., Vespignani, A.: Studying the emerging global brain: analyzing and visualizing the impact of co-authorship teams. Complexity 10(4), 57–67 (2005)CrossRef

Bornmann, L., Leydesdorff, L., Wang, J.: How to improve the prediction based on citation impact percentiles for years shortly after the publication date? J. Inf. 8(1), 175–180 (2014)

Bornmann, L., Mutz, R., Hug, S.E., Daniel, H.P.: A multilevel meta-analysis of studies reporting correlations between the \(h\) index and 37 different \(h\) index variants. J. Inf. 5(3), 346–359 (2011)

Brizan, D.G., Gallagher, K., Jahangir, A., Brown, T.: Predicting citation patterns: defining and determining influence. Scientometrics 108(1), 183–200 (2016)CrossRef

Cao, X., Chen, Y., Liu, K.R.: A data analytic approach to quantifying scientific impact. J. Inf. 10(2), 471–484 (2016)

Chakraborty, T., Kumar, S., Goyal, P., Ganguly, N., Mukherjee, A.: Towards a stratified learning approach to predict future citation counts. In: Proceedings 14th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), pp. 351–360 (2014)

Chakraborty, T., Kumar, S., Goyal, P., Ganguly, N., Mukherjee, A.: On the categorization of scientific citation profiles in computer science. Commun. ACM 58(9), 82–90 (2015)CrossRef

Chaudhuri, S., Dayal, U., Narasayya, V.: An overview of business intelligence technology. Commun. ACM 54(8), 88–98 (2011)CrossRef

10.

Davletov, F., Aydin, A.S., Cakmak, A.: High impact academic paper prediction using temporal and topological features. In: Proceedings 23rd ACM International Conference on Conference on Information and Knowledge Management (CIKM), pp. 491–498 (2014)

11.

Dong, Y., Johnson, R.A., Chawla, N.V.: Can scientific impact be predicted? IEEE Trans. Big Data 2(1), 18–30 (2016)CrossRef

12.

Garner, J., Porter, A.L., Newman, N.C.: Distance and velocity measures: using citations to determine breadth and speed of research impact. Scientometrics 100(3), 687–703 (2014)CrossRef

13.

Hirsch, J.E.: An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. 102(46), 16569–16572 (2005)CrossRef

14.

Jones, B.F., Weinberg, B.A.: Age dynamics in scientific creativity. Proc. Natl. Acad. Sci. 108(47), 18910–18914 (2011)CrossRef

15.

Ke, Q., Ferrara, E., Radicchi, F., Flammini, A.: Defining and identifying sleeping beauties in science. Proc. Natl. Acad. Sci. 112(24), 7426–7431 (2015)CrossRef

16.

Klimek, P.S., Jovanovic, A., Egloff, R., Schneider, R.: Successful fish go with the flow: citation impact prediction based on centrality measures for term-document networks. Scientometrics 107(3), 1265–1282 (2016)CrossRef

17.

Laurance, W.F., Useche, D.C., Laurance, S.G., Bradshaw, C.J.: Predicting publication success for biologists. Bioscience 63(10), 817 (2013)CrossRef

18.

Li, J., Shi, D., Zhao, S.X., Ye, F.Y.: A study of the “heartbeat spectra” for “sleeping beauties”. J. Inf. 8(3), 493–502 (2014)

19.

Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Phys. A Stat. Mech. Appl. 390(6), 1150–1170 (2011)CrossRef

20.

Mazloumian, A.: Predicting scholars’ scientific impact. PLoS ONE 7(11), 1–5 (2012)CrossRef

21.

McNamara, D., Wong, P., Christen, P., Ng, K.S.: Predicting high impact academic papers using citation network features. In: Li, J., Cao, L., Wang, C., Tan, K.C., Liu, B., Pei, J., Tseng, V.S. (eds.) PAKDD 2013. LNCS, vol. 7867, pp. 14–25. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40319-4_2CrossRef

22.

Merton, R.K.: The Matthew effect in science. Science 159(3810), 56–63 (1968)CrossRef

23.

Nezhadbiglari, M., Gonçalves, M.A., Almeida, J.M.: Early prediction of scholar popularity. In: Proceedings 16th ACM/IEEE-CS on Joint Conference on Digital Libraries (JCDL), pp. 181–190 (2016)

24.

Penner, O., Pan, R.K., Petersen, A.M., Fortunato, S.: The case for caution in predicting scientists’ future impact. Phys. Today 66(4), 8 (2013)CrossRef

25.

Pobiedina, N., Ichise, R.: Citation count prediction as a link prediction problem. Appl. Intell. 44(2), 252–268 (2016)CrossRef

26.

Pradhan, D., Paul, P.S., Maheswari, U., Nandi, S., Chakraborty, T.: C3-index: revisiting author’s performance measure. In: Proceedings 8th ACM Conference on Web Science (WebSci), pp. 318–319 (2016)

27.

van Raan, A.F.J.: Sleeping beauties in science. Scientometrics 59(3), 467–472 (2004)CrossRef

28.

Revesz, P.Z.: A method for predicting citations to the scientific publications of individual researchers. In: Proceedings 18th International Database Engineering and Applications Symposium (IDEAS), pp. 9–18 (2014)

29.

Revesz, P.Z.: Data mining citation databases: a new index measure that predicts Nobel prizewinners. In: Proceedings 19th International Database Engineering and Applications Symposium (IDEAS), pp. 1–9 (2015)

30.

Sayyadi, H., Getoor, L.: Futurerank: ranking scientific articles by predicting their future pagerank. In: Proceedings SIAM International Conference on Data Mining (SDM), pp. 533–544 (2009)CrossRef

31.

Schreiber, M.: How relevant is the predictive power of the \(h\)-index? A case study of the time-dependent Hirsch index. J. Inf. 7(2), 325–329 (2013)MathSciNet

32.

Sidiropoulos, A., Gogoglou, A., Katsaros, D., Manolopoulos, Y.: Gazing at the skyline for star scientists. J. Inf. 10(3), 789–813 (2016)

33.

Sidiropoulos, A., Manolopoulos, Y.: A citation-based system to assist prize awarding. ACM SIGMOD Rec. 34(4), 54–60 (2005)CrossRef

34.

Sinatra, R., Wang, D., Deville, P., Song, C., Barabási, A.: Quantifying the evolution of individual scientific impact. Science 354(6312), aaf5239 (2016)CrossRef

35.

de Solla Price, D.J.: Networks of scientific papers. Science 149(3683), 510–515 (1965)CrossRef

36.

Vieira, E.S., Cabral, J.A., Gomes, J.A.: How good is a model based on bibliometric indicators in predicting the final decisions made by peers? J. Inf. 8(2), 390–405 (2014)

37.

Wang, S., Xie, S., Zhang, X., Li, Z., Yu, P.S., He, Y.: Coranking the future influence of multiobjects in bibliographic network through mutual reinforcement. ACM Trans. Intell. Syst. Technol. 7(4), 64:1–64:28 (2016)CrossRef

38.

Way, S.F., Morgan, A.C., Clauset, A., Larremore, D.B.: The misleading narrative of the canonical faculty productivity trajectory. CoRR abs/1612.08228 (2016)

39.

Wildgaard, L., Schneider, J.W., Larsen, B.: A review of the characteristics of \(108\) author-level bibliometric indicators. Scientometrics 101(1), 125–158 (2014)CrossRef

40.

Xiao, S., Yan, J., Li, C., Jin, B., Wang, X., Yang, X., Chu, S.M., Zhu, H.: On modeling and predicting individual paper citation count over time. In: Proceedings 25th International Joint Conference on Artificial Intelligence (IJCAI), pp. 2676–2682 (2016)

41.

Zhang, J., Ning, Z., Bai, X., Wang, W., Yu, S., Xia, F.: Who are the rising stars in academia? In: Proceedings 16th ACM/IEEE-CS on Joint Conference on Digital Libraries (JCDL), pp. 211–212 (2016)

Titel: Predicting the Evolution of Scientific Output
verfasst von: Antonia Gogoglou
Yannis Manolopoulos
Verlag: Springer International Publishing
Buch: Computational Collective Intelligence
Print ISBN: 978-3-319-67073-7

Electronic ISBN: 978-3-319-67074-4

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-67074-4_24

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"