Prediction and application of article potential citations based on nonlinear citation-forecasting combined model (original) (raw)

References

• Abrishami, A., & Aliakbary, S. (2019). Predicting citation counts based on deep neural network learning techniques. Journal of Informetrics, 13(2), 485–499. https://doi.org/10.1016/j.joi.2019.02.011.
  Article Google Scholar
• Aladag, C. H., Egrioglu, E., & Yolcu, U. (2010). Forecast combination by using artificial neural networks. Neural Processing Letters, 32(3), 269–276. https://doi.org/10.1007/s11063-010-9156-7.
  Article MATH Google Scholar
• Avramescu, A. (2010). Actuality and obsolescence of scientific literature. Journal of the American Society for Information Science and Technology, 30(5), 296–303.
  Article Google Scholar
• Bai, X., Liu, H., Zhang, F., Ning, Z., Kong, X., Lee, I., & Xia, F. (2017). An overview on evaluating and predicting scholarly article impact. Information, 8(3), 73. https://doi.org/10.3390/info8030073.
  Article Google Scholar
• Bai, X., Zhang, F., & Lee, I. (2019). Predicting the citations of scholarly paper. Journal of Informetrics, 13, 407–418. https://doi.org/10.1016/j.joi.2019.01.010.
  Article Google Scholar
• Bates, J. M., & Granger, C. W. J. (2001). The combination of forecasts. Cambridge: Cambridge University Press.
  Book Google Scholar
• Bello, I., Pham, H., Le, Q.V., Norouzi, M., & Bengio, S.: Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940 (2016). https://arxiv.org/abs/1611.09940
• Bose, B. K. (2007). Neural network applications in power electronics and motor drives-an introduction and perspective. IEEE Transactions on Industrial Electronics, 54(1), 14–33. https://doi.org/10.1109/TIE.2006.888683.
  Article Google Scholar
• Cao, X., Chen, Y., & Liu, K. R. (2016). A data analytic approach to quantifying scientific impact. Journal of Informetrics, 10(2), 471–484.
  Article Google Scholar
• Chakraborty, T., Kumar, S., Goyal, P., Ganguly, N., & Mukherjee, A.: Towards a stratified learning approach to predict future citation counts. In: IEEE/ACM Joint Conference on Digital Libraries, pp. 351–360 (2014). https://doi.org/10.1109/JCDL.2014.6970190
• Chen, J., Zhang, C.: Predicting citation counts of papers. In: 2015 IEEE 14th International Conference on Cognitive Informatics Cognitive Computing (ICCI*CC), pp. 434–440 (2015). https://doi.org/10.1109/ICCI-CC.2015.7259421
• Chen, K. Y. (2011). Combining linear and nonlinear model in forecasting tourism demand. Expert Systems with Applications, 38(8), 10368–10376. https://doi.org/10.1016/j.eswa.2011.02.049.
  Article Google Scholar
• Clauset, A., Larremore, D. B., & Sinatra, R. (2017). Data-driven predictions in the science of science. Science, 355(6324), 477–480. https://doi.org/10.1126/science.aal4217.
  Article Google Scholar
• Cressey, Daniel. (2015). sleeping beauty papers slumber for decades. Nature.
• Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152. https://doi.org/10.1007/s11192-006-0144-7.
  Article MathSciNet Google Scholar
• Garfield, E. (1972). Citation analysis as a tool in journal evaluation. Science, 178(4060), 471–479.
  Article Google Scholar
• Garfield, E. (2006). The history and meaning of the journal impact factor. Journal of the American Medical Association, 295(1), 90–93.
  Article Google Scholar
• Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572. https://doi.org/10.1073/pnas.0507655102.
  Article MATH Google Scholar
• Jin, B., Liang, L., Rousseau, R., & Egghe, L. (2007). The r-and ar-indices: Complementing the h-index. Chinese science bulletin, 52(6), 855–863. https://doi.org/10.1007/s11434-007-0145-9.
  Article Google Scholar
• Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with Applications, 37(1), 479–489. https://doi.org/10.1016/j.eswa.2009.05.044.
  Article MATH Google Scholar
• Kolidakis, S., Botzoris, G., Profillidis, V., & Lemonakis, P. (2019). Road traffic forecasting- a hybrid approach combining artificial neural network with singular spectrum analysis. Economic Analysis and Policy, 64, 159–171. https://doi.org/10.1016/j.eap.2019.08.002.
  Article Google Scholar
• Lariviere, V., Archambault, E., & Gingras, Y. (2010). Long-term variations in the aging of scientific literature: From exponential growth to steady-state science (1900–2004). Journal of the American Society for Information Science and Technology, 59(2), 288–296.
  Article Google Scholar
• Lawrence, S., Giles, C. L., Tsoi, Ah Chung, & Back, A. D. (1997). Face recognition: A convolutional neural-network approach. IEEE Transactions on Neural Networks, 8(1), 98–113. https://doi.org/10.1109/72.554195.
  Article Google Scholar
• Liu, D. C., & Nocedal, J. (1989). On the limited memory bfgs method for large scale optimization. Mathematical Programming, 45(1), 503–528.
  Article MathSciNet Google Scholar
• Liu, X.: A grey neural network and input-output combined forecasting model and its application in primary energy related co2 emissions estimation by sector in china. Energy Procedia 36, 815 – 824 (2013). https://doi.org/10.1016/j.egypro.2013.07.094. TerraGreen 13 International Conference 2013 - Advancements in Renewable Energy and Clean Environment
• Liu, X., Moreno, B., & Garcí, A. S. (2016). A grey neural network and input-output combined forecasting model. primary energy consumption forecasts in spanish economic sectors. Energy, 115, 1042–1054. https://doi.org/10.1016/j.energy.2016.09.017.
  Article Google Scholar
• Melin, P., & Sínchez, D. (2018). Multi-objective optimization for modular granular neural networks applied to pattern recognition. Information Sciences, 460–461, 594–610. https://doi.org/10.1016/j.ins.2017.09.031.
  Article MathSciNet Google Scholar
• Moed, H. F., Colledge, L., Reedijk, J., Moya-Anegon, F., Guerrero-Bote, V., Plume, A., & Amin, M. (2012). Citation-based metrics are appropriate tools in journal assessment provided that they are accurate and used in an informed way. Scientometrics, 92(2), 367–376. https://doi.org/10.1007/s11192-012-0679-8.
  Article Google Scholar
• Molinari, J. F., & Molinari, A. (2008). A new methodology for ranking scientific institutions. Scientometrics, 75(1), 163–174. https://doi.org/10.1007/s11192-007-1853-2.
  Article MathSciNet Google Scholar
• Newman, M. E. J. (2009). The first-mover advantage in scientific publication. EPL (Europhysics Letters), 86(6), 68001. https://doi.org/10.1209/0295-5075/86/68001.
  Article Google Scholar
• Pobiedina, N., & Ichise, R. (2016). Citation count prediction as a link prediction problem. Applied Intelligence, 44(2), 252–268. https://doi.org/10.1007/s10489-015-0657-y.
  Article Google Scholar
• Qing KE, Emilio F, Filippo, R. (2015). Alessandro, & Flammini: Defining and identifying sleeping beauties in science. Proceedings of the National Academy of Sciences of the United States of America.
• Redner, S. (1998). How popular is your paper? an empirical study of the citation distribution. European Physical Journal B, 4(2), 131–134. https://doi.org/10.1007/s100510050359.
  Article Google Scholar
• Tahamtan, I., Afshar, A. S., & Ahamdzadeh, K. (2016). Factors affecting number of citations: A comprehensive review of the literature. Scientometrics, 107(3), 1195–1225. https://doi.org/10.1007/s11192-016-1889-2.
  Article Google Scholar
• Teshnehlab, M., & Kyoko, W. (2013). Intelligent control based on flexible neural networks,. Springer Science & Business Media.
• Van Raan, A. F. (2004). Sleeping beauties in science. Scientometrics, 59(3), 467–472. https://doi.org/10.1023/B:SCIE.0000018543.82441.f1.
  Article Google Scholar
• Waltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10(2), 365–391. https://doi.org/10.1016/j.joi.2016.02.007.
  Article Google Scholar
• Wang, Mingyang, Chen, Guangsheng, Li, & Shi: Detecting latent referential articles based on their vitality performance in the latest 2 years. Scientometrics An International Journal for All Quantitative Aspects of the Science of Science Policy (2017)
• Wang, D., Song, C., & Barabási, A. L. (2013). Quantifying long-term scientific impact. Science, 342(6154), 127–132. https://doi.org/10.1126/science.1237825.
  Article Google Scholar
• Wang, J., Mei, Y., & Hicks, D. (2014). Comment on quantifying long-term scientific impact. Science, 345(6193), 149–149. https://doi.org/10.1126/science.1248770.
  Article Google Scholar
• Wang, J., Zhu, S., Zhang, W., & Lu, H.: Combined modeling for electric load forecasting with adaptive particle swarm optimization. Energy 35(4), 1671–1678 (2010). https://doi.org/10.1016/j.energy.2009.12.015. Demand Response Resources: the US and International Experience
• Wang, M., Wang, Z., & Chen, G. (2019). Which can better predict the future success of articles? bibliometric indices or alternative metrics. Scientometrics.
• Webber, W., Moffat, A., & Zobel, J.: A similarity measure for indefinite rankings. ACM Trans. Inf. Syst. 28(4) (2010). https://doi.org/10.1145/1852102.1852106
• Wildgaard, L., Schneider, J. W., & Larsen, B. (2014). A review of the characteristics of 108 author-level bibliometric indicators. Scientometrics, 101(1), 125–158. https://doi.org/10.1007/s11192-014-1423-3.
  Article Google Scholar
• Xu, J., Li, M., Jiang, J., Ge, B., & Cai, M. (2019). Early prediction of scientific impact based on multi-bibliographic features and convolutional neural network. IEEE Access, 7, 92248–92258. https://doi.org/10.1109/ACCESS.2019.2927011.
  Article Google Scholar
• Yu, T., Yu, G., Li, P. Y., & Wang, L. (2014). Citation impact prediction for scientific papers using stepwise regression analysis. Scientometrics, 101(2), 1233–1252. https://doi.org/10.1007/s11192-014-1279-6.
  Article Google Scholar
• Yuan, S., Tang, J., Zhang, Y., Wang, Y., & DXiao, T.: Modeling and predicting citation count via recurrent neural network with long short-term memory. CoRR abs/1811.02129 (2018)
• Zeng, A., Shen, Z., Zhou, J., Wu, J., Fan, Y., Wang, Y., & Stanley, H. E. (2017). The science of science: From the perspective of complex systems. Physics Reports, 714—-715, 1–73.
  Article MathSciNet Google Scholar
• Zhang, G. (2003). Time series forecasting using a hybrid arima and neural network model. Neurocomputing, 50, 159–175.
  Article Google Scholar
• Zhou, Z., Si, G., Zheng, K., Xu, X., Qu, K., & Zhang, Y. (2019). Cmbcf: A cloud model based hybrid method for combining forecast. Applied Soft Computing, 85, 105766.
  Article Google Scholar

Download references