IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v564y2021ics0378437120308165.html
   My bibliography  Save this article

Adaptive topological coevolution of interdependent networks: Scientific collaboration-citation networks as an example

Author

Listed:
  • Liu, Xiao Fan
  • Chen, Hou-Jin
  • Sun, Wu-Jiu

Abstract

Topological evolution is a ubiquitous feature of complex networks. In a set of coupled networks, either with the same or different types of nodes, the topological evolution of a network may have a profound influence on the others. This work i) reports observations of adaptive topological coevolution between the scientific collaboration and paper citation networks, and ii) proposes a concise framework of modeling the coevolution phenomenon between the interdependent networks. Adaptation to structural evolutions from its counterparts is a crucial topological evolutionary mechanism of a complex network. This work provides a new perspective for a better understanding of the driving forces of topological evolution and may further help with applications, such as link prediction and failure mitigation, in complex networks.

Suggested Citation

  • Liu, Xiao Fan & Chen, Hou-Jin & Sun, Wu-Jiu, 2021. "Adaptive topological coevolution of interdependent networks: Scientific collaboration-citation networks as an example," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
  • Handle: RePEc:eee:phsmap:v:564:y:2021:i:c:s0378437120308165
    DOI: 10.1016/j.physa.2020.125518
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120308165
    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.2020.125518?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. Réka Albert & Hawoong Jeong & Albert-László Barabási, 1999. "Diameter of the World-Wide Web," Nature, Nature, vol. 401(6749), pages 130-131, September.
    2. Paulo R. Guimarães & Mathias M. Pires & Pedro Jordano & Jordi Bascompte & John N. Thompson, 2017. "Indirect effects drive coevolution in mutualistic networks," Nature, Nature, vol. 550(7677), pages 511-514, October.
    3. Smolyak, Alex & Levy, Orr & Shekhtman, Louis & Havlin, Shlomo, 2018. "Interdependent networks in Economics and Finance—A Physics approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 612-619.
    4. Hajra, Kamalika Basu & Sen, Parongama, 2006. "Modelling aging characteristics in citation networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 368(2), pages 575-582.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. 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.
    2. Cao, Huiying & Gao, Chao & Wang, Zhen, 2023. "Ranking academic institutions by means of institution–publication networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    3. Yan, Li & Cao, Huiying & Gao, Chao & Wang, Zhen & Li, Xuelong, 2023. "Mining of book-loan behavior based on coupling relationship analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).

    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. Wen, Guanghui & Duan, Zhisheng & Chen, Guanrong & Geng, Xianmin, 2011. "A weighted local-world evolving network model with aging nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 4012-4026.
    2. Mohd-Zaid, Fairul & Kabban, Christine M. Schubert & Deckro, Richard F. & White, Edward D., 2017. "Parameter specification for the degree distribution of simulated Barabási–Albert graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 141-152.
    3. Chen, Shu-Heng & Chang, Chia-Ling & Wen, Ming-Chang, 2014. "Social networks and macroeconomic stability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 8, pages 1-40.
    4. Zhang, Wen-Yao & Wei, Zong-Wen & Wang, Bing-Hong & Han, Xiao-Pu, 2016. "Measuring mixing patterns in complex networks by Spearman rank correlation coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 440-450.
    5. Pi, Xiaochen & Tang, Longkun & Chen, Xiangzhong, 2021. "A directed weighted scale-free network model with an adaptive evolution mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    6. He, He & Yang, Bo & Hu, Xiaoming, 2016. "Exploring community structure in networks by consensus dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 342-353.
    7. Long Ma & Xiao Han & Zhesi Shen & Wen-Xu Wang & Zengru Di, 2015. "Efficient Reconstruction of Heterogeneous Networks from Time Series via Compressed Sensing," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-12, November.
    8. Blagus, Neli & Šubelj, Lovro & Bajec, Marko, 2012. "Self-similar scaling of density in complex real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2794-2802.
    9. Elias Carroni & Paolo Pin & Simone Righi, 2020. "Bring a Friend! Privately or Publicly?," Management Science, INFORMS, vol. 66(5), pages 2269-2290, May.
    10. Kaihao Liang & Shuliang Li & Wenfeng Zhang & Zhuokui Wu & Jiaying He & Mengmeng Li & Yuling Wang, 2024. "Evolution of Complex Network Topology for Chinese Listed Companies Under the COVID-19 Pandemic," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1121-1136, March.
    11. Biggiero, Lucio & Angelini, Pier Paolo, 2015. "Hunting scale-free properties in R&D collaboration networks: Self-organization, power-law and policy issues in the European aerospace research area," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 21-43.
    12. Duan, Shuyu & Wen, Tao & Jiang, Wen, 2019. "A new information dimension of complex network based on Rényi entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 529-542.
    13. Dávid Csercsik & Sándor Imre, 2017. "Cooperation and coalitional stability in decentralized wireless networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 64(4), pages 571-584, April.
    14. Baek, Seung Ki & Kim, Tae Young & Kim, Beom Jun, 2008. "Testing a priority-based queue model with Linux command histories," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3660-3668.
    15. Jing Yang & Yingwu Chen, 2011. "Fast Computing Betweenness Centrality with Virtual Nodes on Large Sparse Networks," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-5, July.
    16. Freddy Hernán Cepeda López, 2008. "La topología de redes como herramienta de seguimiento en el Sistema de Pagos de Alto Valor en Colombia," Borradores de Economia 513, Banco de la Republica de Colombia.
    17. Chung-Yuan Huang & Chuen-Tsai Sun & Hsun-Cheng Lin, 2005. "Influence of Local Information on Social Simulations in Small-World Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-8.
    18. Xiang, Wang, 2023. "Strong ties or structural holes? A distance distribution perspective," Economics Letters, Elsevier, vol. 229(C).
    19. Xue Guo & Hu Zhang & Tianhai Tian, 2019. "Multi-Likelihood Methods for Developing Stock Relationship Networks Using Financial Big Data," Papers 1906.08088, arXiv.org.
    20. Chang, Chia-ling & Chen, Shu-heng, 2011. "Interactions in DSGE models: The Boltzmann-Gibbs machine and social networks approach," Economics Discussion Papers 2011-25, Kiel Institute for the World Economy (IfW Kiel).

    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:564:y:2021:i:c:s0378437120308165. 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: 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.