IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v387y2008i18p4692-4698.html
   My bibliography  Save this article

Measuring the preferential attachment mechanism in citation networks

Author

Listed:
  • Wang, Mingyang
  • Yu, Guang
  • Yu, Daren

Abstract

In this paper, we investigated the preferential attachment mechanism (PAM) by considering the dynamic property in papers’ in-degree k for three citation networks. We found that the past citations obtained in different years will have different influences on papers’ attachment rate Π(k,t). We proposed two methods to consider these different influences. One is the Gradually-vanishing Memory Preferential Attachment Mechanism (GMPAM) based on weighted past citations. The other is the Short-term Memory Preferential Attachment Mechanism (SMPAM) based on citations obtained in the recent one-year period. Experiments showed that SMPAM is simpler and more universal in practice. We can just calculate the citations to papers in the recent one-year period to study the papers’ attachment property.

Suggested Citation

  • Wang, Mingyang & Yu, Guang & Yu, Daren, 2008. "Measuring the preferential attachment mechanism in citation networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(18), pages 4692-4698.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:18:p:4692-4698
    DOI: 10.1016/j.physa.2008.03.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437108003208
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2008.03.017?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Peng, Tai-Quan, 2015. "Assortative mixing, preferential attachment, and triadic closure: A longitudinal study of tie-generative mechanisms in journal citation networks," Journal of Informetrics, Elsevier, vol. 9(2), pages 250-262.
    2. Ren, Fu-Xin & Shen, Hua-Wei & Cheng, Xue-Qi, 2012. "Modeling the clustering in citation networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(12), pages 3533-3539.
    3. Dong, Ke & Wu, Jiang & Wang, Kaili, 2021. "On the inequality of citation counts of all publications of individual authors," Journal of Informetrics, Elsevier, vol. 15(4).
    4. Adilson Vital & Diego R. Amancio, 2022. "A comparative analysis of local similarity metrics and machine learning approaches: application to link prediction in author citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 6011-6028, October.
    5. Bai, Xiaomei & Zhang, Fuli & Lee, Ivan, 2019. "Predicting the citations of scholarly paper," Journal of Informetrics, Elsevier, vol. 13(1), pages 407-418.
    6. Wu, Yan & Fu, Tom Z.J. & Chiu, Dah Ming, 2014. "Generalized preferential attachment considering aging," Journal of Informetrics, Elsevier, vol. 8(3), pages 650-658.
    7. Kong, Ling & Wang, Dongbo, 2020. "Comparison of citations and attention of cover and non-cover papers," Journal of Informetrics, Elsevier, vol. 14(4).
    8. Binglu Wang & Yi Bu & Yang Xu, 2018. "A quantitative exploration on reasons for citing articles from the perspective of cited authors," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 675-687, August.
    9. Li, Bo & Sun, Duoyong & Bai, Guanghan, 2017. "Empirical research on evolutionary behavior of covert network with preference measurement," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 33-43.
    10. Guillermo Armando Ronda-Pupo & J. Sylvan Katz, 2017. "The scaling relationship between degree centrality of countries and their citation-based performance on Management Information Systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1285-1299, September.
    11. Choi, Jinho & Hwang, Yong-Sik, 2014. "Patent keyword network analysis for improving technology development efficiency," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 170-182.
    12. Jorge A. V. Tohalino & Laura V. C. Quispe & Diego R. Amancio, 2021. "Analyzing the relationship between text features and grants productivity," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4255-4275, May.
    13. He, Chaocheng & Liu, Fuzhen & Dong, Ke & Wu, Jiang & Zhang, Qingpeng, 2023. "Research on the formation mechanism of research leadership relations: An exponential random graph model analysis approach," Journal of Informetrics, Elsevier, vol. 17(2).
    14. Xiomara S. Q. Chacon & Thiago C. Silva & Diego R. Amancio, 2020. "Comparing the impact of subfields in scientific journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 625-639, October.
    15. Colman, E.R. & Rodgers, G.J., 2013. "Complex scale-free networks with tunable power-law exponent and clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5501-5510.
    16. Inoue, Masaaki & Pham, Thong & Shimodaira, Hidetoshi, 2020. "Joint estimation of non-parametric transitivity and preferential attachment functions in scientific co-authorship networks," Journal of Informetrics, Elsevier, vol. 14(3).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:387:y:2008:i:18:p:4692-4698. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.