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Topological analysis of citation networks to discover the future core articles

Author

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  • Naoki Shibata
  • Yuya Kajikawa
  • Katsumori Matsushima

Abstract

In this article, we investigated the factors determining the capability of academic articles to be cited in the future using a topological analysis of citation networks. The basic idea is that articles that will have many citations were in a “similar” position topologically in the past. To validate this hypothesis, we investigated the correlation between future times cited and three measures of centrality: clustering centrality, closeness centrality, and betweenness centrality. We also analyzed the effect of aging as well as of self‐correlation of times cited. Case studies were performed in the two following recent representative innovations: Gallium Nitride and Complex Networks. The results suggest that times cited is the main factor in explaining the near future times cited, and betweenness centrality is correlated with the distant future times cited. The effect of topological position on the capability to be cited is influenced by the migrating phenomenon in which the activated center of research shifts from an existing domain to a new emerging domain.

Suggested Citation

  • Naoki Shibata & Yuya Kajikawa & Katsumori Matsushima, 2007. "Topological analysis of citation networks to discover the future core articles," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(6), pages 872-882, April.
    • Handle: RePEc:bla:jamist:v:58:y:2007:i:6:p:872-882
    DOI: 10.1002/asi.20529
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    Cited by:

    1. Luo, Zhuoran & Lu, Wei & He, Jiangen & Wang, Yuqi, 2022. "Combination of research questions and methods: A new measurement of scientific novelty," Journal of Informetrics, Elsevier, vol. 16(2).
    2. Jose Torres-Pruñonosa & Miquel Angel Plaza-Navas & Francisco Díez-Martín & Albert Beltran-Cangrós, 2021. "The Intellectual Structure of Social and Sustainable Public Procurement Research: A Co-Citation Analysis," Sustainability, MDPI, vol. 13(2), pages 1-33, January.
    3. Gregorio González-Alcaide & Pedro Llorente & José M. Ramos, 2016. "Bibliometric indicators to identify emerging research fields: publications on mass gatherings," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 1283-1298, November.
    4. Gao, Qiang & Liang, Zhentao & Wang, Ping & Hou, Jingrui & Chen, Xiuxiu & Liu, Manman, 2021. "Potential index: Revealing the future impact of research topics based on current knowledge networks," Journal of Informetrics, Elsevier, vol. 15(3).
    5. Daria Maltseva & Vladimir Batagelj, 2021. "Journals publishing social network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3593-3620, April.
    6. Alvarez-Meaza, Izaskun & Zarrabeitia-Bilbao, Enara & Rio-Belver, Rosa-María & Garechana-Anacabe, Gaizka, 2021. "Green scheduling to achieve green manufacturing: Pursuing a research agenda by mapping science," Technology in Society, Elsevier, vol. 67(C).
    7. Sotaro Shibayama & Jian Wang, 2020. "Measuring originality in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 409-427, January.
    8. Chaomei Chen & Zhigang Hu & Jared Milbank & Timothy Schultz, 2013. "A visual analytic study of retracted articles in scientific literature," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(2), pages 234-253, February.
    9. Liu, Xiang & Jiang, Tingting & Ma, Feicheng, 2013. "Collective dynamics in knowledge networks: Emerging trends analysis," Journal of Informetrics, Elsevier, vol. 7(2), pages 425-438.
    10. Mariana Reis Maria & Rosangela Ballini & Roney Fraga Souza, 2023. "Evolution of Green Finance: A Bibliometric Analysis through Complex Networks and Machine Learning," Sustainability, MDPI, vol. 15(2), pages 1-23, January.
    11. Min, Chao & Bu, Yi & Sun, Jianjun, 2021. "Predicting scientific breakthroughs based on knowledge structure variations," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    12. Haiko Lietz, 2020. "Drawing impossible boundaries: field delineation of Social Network Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2841-2876, December.
    13. Aryuna Kim & Daria Maltseva, 2024. "Qualitative social network analysis: studying the field through the bibliographic approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 385-411, February.
    14. Zhao, Star X. & Ye, Fred Y., 2012. "Exploring the directed h-degree in directed weighted networks," Journal of Informetrics, Elsevier, vol. 6(4), pages 619-630.
    15. Jianhua Hou & Xiucai Yang & Chaomei Chen, 2020. "Measuring researchers’ potential scholarly impact with structural variations: Four types of researchers in information science (1979–2018)," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-26, June.
    16. Chen, Chaomei & Chen, Yue & Horowitz, Mark & Hou, Haiyan & Liu, Zeyuan & Pellegrino, Donald, 2009. "Towards an explanatory and computational theory of scientific discovery," Journal of Informetrics, Elsevier, vol. 3(3), pages 191-209.
    17. Yoshiyuki Takeda & Yuya Kajikawa, 2010. "Tracking modularity in citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(3), pages 783-792, June.
    18. Chabowski, Brian R. & Samiee, Saeed, 2023. "A bibliometric examination of the literature on emerging market MNEs as the basis for future research," Journal of Business Research, Elsevier, vol. 155(PB).
    19. Daria Maltseva & Vladimir Batagelj, 2022. "Collaboration between authors in the field of social network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3437-3470, June.
    20. Francisco Díez-Martín & Alicia Blanco-González & Camilo Prado-Román, 2021. "The intellectual structure of organizational legitimacy research: a co-citation analysis in business journals," Review of Managerial Science, Springer, vol. 15(4), pages 1007-1043, May.
    21. Yuya Kajikawa, 2022. "Reframing evidence in evidence-based policy making and role of bibliometrics: toward transdisciplinary scientometric research," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5571-5585, September.

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