IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v114y2018icp461-467.html
   My bibliography  Save this article

The agglomeration phenomenon influence on the scaling law of the scientific collaboration system

Author

Listed:
  • Shen, Ai-Zhong
  • Guo, Jin-Li
  • Wu, Guo-Lin
  • Jia, Shu-Wei

Abstract

This paper presents a scientific collaboration hypernetwork evolution model with adjustable clustering coefficient in which the authors are regarded as nodes and the hyperedges are the cooperative articles. Firstly, we build the scientific cooperation hypernetwork through the real paper data which comes from the database of arxive. The empirical results show that the node's hyperdegree follows the power law distribution, but the hyperedge's node-degree has the exponent distribution. In addition, we establish the scientific collaboration clustering hypernetwork evolution model by employing the Poisson process theory and the continuous method for studying the agglomeration phenomenon of scientific cooperation system. The theoretical analysis shows that the node's hyperdegree distribution has scale-free characteristics. The power index of our model is independent of the clustering coefficient, and the theoretical analyses agree with the conducted numerical simulations. Moreover, our model not only describes the scale-free property, but also depicts the phenomenon of agglomeration. Both the properties are usually coexist in the scientific cooperation, our model is more actual.

Suggested Citation

  • Shen, Ai-Zhong & Guo, Jin-Li & Wu, Guo-Lin & Jia, Shu-Wei, 2018. "The agglomeration phenomenon influence on the scaling law of the scientific collaboration system," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 461-467.
  • Handle: RePEc:eee:chsofr:v:114:y:2018:i:c:p:461-467
    DOI: 10.1016/j.chaos.2018.07.037
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077918307252
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2018.07.037?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.

    References listed on IDEAS

    as
    1. M. E. J. Newman & D. J. Watts, 1999. "Scaling and Percolation in the Small-World Network Model," Working Papers 99-05-034, Santa Fe Institute.
    2. Suo, Qi & Guo, Jin-Li & Shen, Ai-Zhong, 2018. "Information spreading dynamics in hypernetworks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 475-487.
    3. Tao Zhou & Bing-Hong Wang & Ying-Di Jin & Da-Ren He & Pei-Pei Zhang & Yue He & Bei-Bei Su & Kan Chen & Zhong-Zhi Zhang & Jian-Guo Liu, 2007. "Modelling Collaboration Networks Based On Nonlinear Preferential Attachment," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 297-314.
    4. Hadjikhani, Amjad & Lee, Joong-Woo & Ghauri, Pervez N., 2008. "Network view of MNCs' socio-political behavior," Journal of Business Research, Elsevier, vol. 61(9), pages 912-924, September.
    5. Haiyang Fang & Dali Jiang & Tinghong Yang & Ling Fang & Jian Yang & Wu Li & Jing Zhao, 2018. "Network evolution model for supply chain with manufactures as the core," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-28, January.
    6. Wang, Jiang-Pan & Guo, Qiang & Yang, Guang-Yong & Liu, Jian-Guo, 2015. "Improved knowledge diffusion model based on the collaboration hypernetwork," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 250-256.
    7. Nagurney, Anna & Dong, June & Zhang, Ding, 2002. "A supply chain network equilibrium model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 38(5), pages 281-303, September.
    8. Alireza Abbasi & Liaquat Hossain & Shahadat Uddin & Kim J. R. Rasmussen, 2011. "Evolutionary dynamics of scientific collaboration networks: multi-levels and cross-time analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(2), pages 687-710, November.
    9. Yang, Guang-Yong & Hu, Zhao-Long & Liu, Jian-Guo, 2015. "Knowledge diffusion in the collaboration hypernetwork," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 429-436.
    10. D. H. Zanette & S. Bouzat, 2010. "Potential-partnership networks and the dynamical structure of monogamous populations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 75(3), pages 373-379, June.
    11. Jian-Wei Wang & Li-Li Rong & Qiu-Hong Deng & Ji-Yong Zhang, 2010. "Evolving hypernetwork model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(4), pages 493-498, October.
    12. Luis Cadarso & Ángel Marín, 2017. "Improved rapid transit network design model: considering transfer effects," Annals of Operations Research, Springer, vol. 258(2), pages 547-567, November.
    13. Aimée Backiel & Bart Baesens & Gerda Claeskens, 2016. "Predicting time-to-churn of prepaid mobile telephone customers using social network analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(9), pages 1135-1145, September.
    14. Barabási, A.L & Jeong, H & Néda, Z & Ravasz, E & Schubert, A & Vicsek, T, 2002. "Evolution of the social network of scientific collaborations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 311(3), pages 590-614.
    15. Viana, Matheus P. & Amancio, Diego R. & da F. Costa, Luciano, 2013. "On time-varying collaboration networks," Journal of Informetrics, Elsevier, vol. 7(2), pages 371-378.
    16. Shen, Ai-Zhong & Guo, Jin-Li & Suo, Qi, 2017. "Study of the variable growth hypernetworks influence on the scaling law," Chaos, Solitons & Fractals, Elsevier, vol. 97(C), pages 84-89.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ma, Xiujuan & Ma, Fuxiang & Yin, Jun & Zhao, Haixing, 2018. "Cascading failures of k uniform hyper-network based on the hyper adjacent matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 281-289.
    2. Wang, Zhiping & Yin, Haofei & Jiang, Xin, 2020. "Exploring the dynamic growth mechanism of social networks using evolutionary hypergraph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 544(C).
    3. Wang, Jiang-Pan & Guo, Qiang & Zhou, Lei & Liu, Jian-Guo, 2019. "Dynamic credit allocation for researchers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 208-216.
    4. Chi, Yuxue & Tang, Xianyi & Liu, Yijun, 2022. "Exploring the “awakening effect” in knowledge diffusion: a case study of publications in the library and information science domain," Journal of Informetrics, Elsevier, vol. 16(4).
    5. Xiao Liao & Guangyu Ye & Juan Yu & Yunjiang Xi, 2021. "Identifying lead users in online user innovation communities based on supernetwork," Annals of Operations Research, Springer, vol. 300(2), pages 515-543, May.
    6. Wang, Jiang-Pan & Guo, Qiang & Yang, Guang-Yong & Liu, Jian-Guo, 2015. "Improved knowledge diffusion model based on the collaboration hypernetwork," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 250-256.
    7. Zhang, Haihong & Wu, Wenqing & Zhao, Liming, 2016. "A study of knowledge supernetworks and network robustness in different business incubators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 545-560.
    8. Song, Le & Ma, Yinghong, 2022. "Evaluating tacit knowledge diffusion with algebra matrix algorithm based social networks," Applied Mathematics and Computation, Elsevier, vol. 428(C).
    9. Evangelos Ioannidis & Nikos Varsakelis & Ioannis Antoniou, 2021. "Intelligent Agents in Co-Evolving Knowledge Networks," Mathematics, MDPI, vol. 9(1), pages 1-17, January.
    10. Ioannidis, Evangelos & Varsakelis, Nikos & Antoniou, Ioannis, 2018. "Experts in Knowledge Networks: Central Positioning and Intelligent Selections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 890-905.
    11. Yujia Zhai & Ying Ding & Hezhao Zhang, 2021. "Innovation adoption: Broadcasting versus virality," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(4), pages 403-416, April.
    12. Marian-Gabriel Hâncean & Matjaž Perc & Jürgen Lerner, 2021. "The coauthorship networks of the most productive European researchers," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 201-224, January.
    13. Li Zhai & Xiangbin Yan & Joshana Shibchurn & Xiaohong Song, 2014. "Evolutionary analysis of international collaboration network of Chinese scholars in management research," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1435-1454, February.
    14. Zhao, Liming & Zhang, Haihong & Wu, Wenqing, 2017. "Knowledge service decision making in business incubators based on the supernetwork model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 249-264.
    15. Yu Wei & Sun Ning, 2018. "Establishment and Analysis of the Supernetwork Model for Nanjing Metro Transportation System," Complexity, Hindawi, vol. 2018, pages 1-11, December.
    16. Yue, Zenghui & Xu, Haiyun & Yuan, Guoting & Pang, Hongshen, 2019. "Modeling study of knowledge diffusion in scientific collaboration networks based on differential dynamics: A case study in graphene field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 375-391.
    17. Yang, Jinqing & Bu, Yi & Lu, Wei & Huang, Yong & Hu, Jiming & Huang, Shengzhi & Zhang, Li, 2022. "Identifying keyword sleeping beauties: A perspective on the knowledge diffusion process," Journal of Informetrics, Elsevier, vol. 16(1).
    18. Ioannidis, Evangelos & Varsakelis, Nikos & Antoniou, Ioannis, 2017. "False Beliefs in Unreliable Knowledge Networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 275-295.
    19. Shen, Ai-Zhong & Guo, Jin-Li & Suo, Qi, 2017. "Study of the variable growth hypernetworks influence on the scaling law," Chaos, Solitons & Fractals, Elsevier, vol. 97(C), pages 84-89.
    20. Sergi Lozano & Xosé-Pedro Rodríguez & Alex Arenas, 2014. "Atapuerca: evolution of scientific collaboration in an emergent large-scale research infrastructure," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1505-1520, February.

    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:chsofr:v:114:y:2018:i:c:p:461-467. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.