Abstract
The scientific research behavior of scholars is the core issue of scientific research. The research ideas and methods of complex networks provide a new perspective for the study of science. The scientific citation network and the scientist cooperation network are widely used to study the citation behavior of scholars and the dissemination of scientific ideas, and so far, some results have been obtained. However, due to the lack of information on the content of the article, the research based solely on the network topology has limitations and deficiencies. Combining the textual content analysis through LDA, this paper studies the distribution characteristics of content correlation between articles with citation relations and its evolution with time. It found that the distribution of citation distance has normal characteristics, but the reference distance is visible to be short. Authors have citation preferences for documents at a distance.
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Jia, T., Wang, D., Szymanski, B.K.: Quantifying patterns of research-interest evolution. Nature Human Behaviour 1(4), 0078 (2017)
Leydesdorff, L.: The Challenge of Scientometrics: the Development, Measurement, and Self-Organization of Scientific Communications. Universal-Publishers (2001)
Zeng, A., Shen, Z., Zhou, J., Wu, J., Fan, Y., Wang, Y., Stanley, H.E.: The science of science: from the perspective of complex systems. Phys. Rep. 714, 1–73 (2017)
Newman, M.E.J.: The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. 98(2), 404–409 (2001)
Shibata, N., Kajikawa, Y., Takeda, Y., Matsushima, K.: Detecting emerging research fronts based on topological measures in citation networks of scientific publications. Technovation 28(11), 758–775 (2008)
Radicchi, F., Fortunato, S., Markines, B., Vespignani, A.: Diffusion of scientific credits and the ranking of scientists. Phys. Rev. E 80(5), 056103 (2009)
Li, Y., Li, H., Liu, N., Liu, X.: Important institutions of interinstitutional scientific collaboration networks in materials science. Scientometrics 117(1), 85–103 (2018)
Zhou, Y.B., Lü, L., Li, M.: Quantifying the influence of scientists and their publications: distinguishing between prestige and popularity. New J. Phys. 14(3), 033033 (2012)
An, W., Ding, Y.: The landscape of causal inference: perspective from citation network analysis. Am. Stat. 72(3), 265–277 (2018)
Gualdi, S., Medo, M., Zhang, Y.C.: Influence, originality and similarity in directed acyclic graphs. Europhys. Lett. 96(1), 18004 (2011)
Shen, H.W., Barabási, A.L.: Collective credit allocation in science. Proc. Natl. Acad. Sci. 111(34), 12325–12330 (2014)
Niu, Q., Zhou, J., Zeng, A., Fan, Y., Di, Z.R.: Which publication is your representative work? J. Inf. 10(3), 842–853 (2016)
Son, J., Kim, S.B.: Academic paper recommender system using multilevel simultaneous citation networks. Decis. Support Syst. 105, 24–33 (2018)
Acuna, D.E., Allesina, S., Kording, K.P.: Future impact: predicting scientific success. Nature 489(7415), 201 (2012)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. (CSUR) 34(1), 1–47 (2002)
Saggion, H., Poibeau, T.: Automatic text summarization: past, present and future. In: Multi-source, Multilingual Information Extraction and Summarization, pp. 3–21. Springer Berlin Heidelberg (2013)
Kim, S.N., Medelyan, O., Kan, M.Y., Baldwin, T.: Automatic keyphrase extraction from scientific articles. Lang. Resour. Eval. 47(3), 723–742 (2013)
Blei, D.M., Ng, A., Jordan, M.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2013)
Hantzsche, A., Kara, A., Young, G., Bates, J.M., Granger, C.W., Geweke, J., Amisano, G., Rossi, B., Elliott, G., Timmermann, A.: Latent Dirichlet allocation. Natl. Inst. Econ. Rev. 246(1), F4–F35 (2018)
Acknowledgments
We appreciate comments and helpful suggestions from Prof. Zengru Di, Prof. Chensheng Wu, Ms. Weiwei Gu. This work was supported by Chinese National Natural Science Foundation (71701018, 61673070 and 71671017).
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Li, B., Wang, Y., Li, X., Chen, Q., Bao, J., Zheng, T. (2020). Characteristics and Evolution of Citation Distance Based on LDA Method. In: Xhafa, F., Patnaik, S., Tavana, M. (eds) Advances in Intelligent Systems and Interactive Applications. IISA 2019. Advances in Intelligent Systems and Computing, vol 1084. Springer, Cham. https://doi.org/10.1007/978-3-030-34387-3_37
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DOI: https://doi.org/10.1007/978-3-030-34387-3_37
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