IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v166y2023ics0960077922010591.html
   My bibliography  Save this article

Regulating clustering and assortativity affects node centrality in complex networks

Author

Listed:
  • Wen, Xing-Zhang
  • Zheng, Yue
  • Du, Wen-Li
  • Ren, Zhuo-Ming

Abstract

The limitations of classical node centralities such as degree, closeness, betweenness and eigenvector are rooted in the network topology structure. For a deeper understanding, we regulate the basic network topology structure clustering and assortative coefficient to study the effect on these four classical node centralities. To observe the structural diversity of the complex network, we firstly construct two types of the growing scale-free networks with tunable clustering coefficient and assortative coefficient respectively, and simulate three types of null models on ten real networks to adjust cluster and assortativity. The results indicate that the impact of varying cluster and assortativity on node centrality in complex networks is obvious. We should pay more attention to the network topology when selecting node centralities as identifying the significance or influence of nodes in complex networks.

Suggested Citation

  • Wen, Xing-Zhang & Zheng, Yue & Du, Wen-Li & Ren, Zhuo-Ming, 2023. "Regulating clustering and assortativity affects node centrality in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    • Handle: RePEc:eee:chsofr:v:166:y:2023:i:c:s0960077922010591
    DOI: 10.1016/j.chaos.2022.112880
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077922010591
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2022.112880?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. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. Zhuo-Ming Ren & Xiao Pan & Yi-Cheng Zhang, 2020. "Significance of the Nested Structure in Multiplex World Trade Networks," Complexity, Hindawi, vol. 2020, pages 1-9, December.
    3. Pablo M. Gleiser & Leon Danon, 2003. "Community Structure In Jazz," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 6(04), pages 565-573.
    4. Guo, Qiang & Zhou, Tao & Liu, Jian-Guo & Bai, Wen-Jie & Wang, Bing-Hong & Zhao, Ming, 2006. "Growing scale-free small-world networks with tunable assortative coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 814-822.
    5. Zhong, Lin-Feng & Liu, Quan-Hui & Wang, Wei & Cai, Shi-Min, 2018. "Comprehensive influence of local and global characteristics on identifying the influential nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 78-84.
    6. Maslov, Sergei & Sneppen, Kim & Zaliznyak, Alexei, 2004. "Detection of topological patterns in complex networks: correlation profile of the internet," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 529-540.
    7. Dangalchev, Chavdar, 2006. "Residual closeness in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 365(2), pages 556-564.
    8. Zhong, Lin-Feng & Shang, Ming-Sheng & Chen, Xiao-Long & Cai, Shi-Ming, 2018. "Identifying the influential nodes via eigen-centrality from the differences and similarities of structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 77-82.
    9. Robert M. Bond & Christopher J. Fariss & Jason J. Jones & Adam D. I. Kramer & Cameron Marlow & Jaime E. Settle & James H. Fowler, 2012. "A 61-million-person experiment in social influence and political mobilization," Nature, Nature, vol. 489(7415), pages 295-298, September.
    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. Jianing Li & Jie Fan & Ling Zhu & Xiaohua Wu & Chunyu Luo & Wei Wang, 2023. "Construction of vaccination network and influencing factors: a case study of Chongqing, China," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.

    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. Liu, Panfeng & Li, Longjie & Fang, Shiyu & Yao, Yukai, 2021. "Identifying influential nodes in social networks: A voting approach," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    2. Zhao, Shuying & Sun, Shaowei, 2023. "Identification of node centrality based on Laplacian energy of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    3. Namtirtha, Amrita & Dutta, Animesh & Dutta, Biswanath, 2018. "Identifying influential spreaders in complex networks based on kshell hybrid method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 310-324.
    4. Wang, Zhixiao & Zhao, Ya & Xi, Jingke & Du, Changjiang, 2016. "Fast ranking influential nodes in complex networks using a k-shell iteration factor," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 171-181.
    5. Wang, Jie & Wang, Jun, 2020. "Cross-correlation complexity and synchronization of the financial time series on Potts dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    6. Zareie, Ahmad & Sheikhahmadi, Amir, 2019. "EHC: Extended H-index Centrality measure for identification of users’ spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 141-155.
    7. Salavati, Chiman & Abdollahpouri, Alireza & Manbari, Zhaleh, 2018. "BridgeRank: A novel fast centrality measure based on local structure of the network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 635-653.
    8. Wang, Juan & Li, Chao & Xia, Chengyi, 2018. "Improved centrality indicators to characterize the nodal spreading capability in complex networks," Applied Mathematics and Computation, Elsevier, vol. 334(C), pages 388-400.
    9. Xu, Shuang & Wang, Pei, 2017. "Identifying important nodes by adaptive LeaderRank," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 654-664.
    10. Hu, Jianqiang & Yu, Jie & Cao, Jinde & Ni, Ming & Yu, Wenjie, 2014. "Topological interactive analysis of power system and its communication module: A complex network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 99-111.
    11. Bae, Joonhyun & Kim, Sangwook, 2014. "Identifying and ranking influential spreaders in complex networks by neighborhood coreness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 549-559.
    12. Guijie Zhang & Luning Liu & Yuqiang Feng & Zhen Shao & Yongli Li, 2014. "Cext-N index: a network node centrality measure for collaborative relationship distribution," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 291-307, October.
    13. Zareie, Ahmad & Sheikhahmadi, Amir & Fatemi, Adel, 2017. "Influential nodes ranking in complex networks: An entropy-based approach," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 485-494.
    14. Ebadi, Ashkan & Schiffauerova, Andrea, 2015. "How to become an important player in scientific collaboration networks?," Journal of Informetrics, Elsevier, vol. 9(4), pages 809-825.
    15. Safaei, F. & Yeganloo, H. & Akbar, R., 2020. "Robustness on topology reconfiguration of complex networks: An entropic approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 170(C), pages 379-409.
    16. Yeruva, Sujatha & Devi, T. & Reddy, Y. Samtha, 2016. "Selection of influential spreaders in complex networks using Pareto Shell decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 133-144.
    17. Wang, Zhixiao & Rui, Xiaobin & Yuan, Guan & Cui, Jingjing & Hadzibeganovic, Tarik, 2021. "Endemic information-contagion outbreaks in complex networks with potential spreaders based recurrent-state transmission dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    18. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    19. Zhao, Na & Li, Jie & Wang, Jian & Li, Tong & Yu, Yong & Zhou, Tao, 2020. "Identifying significant edges via neighborhood information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    20. Zhao, Zi-Juan & Guo, Qiang & Yu, Kai & Liu, Jian-Guo, 2020. "Identifying influential nodes for the networks with community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).

    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:166:y:2023:i:c:s0960077922010591. 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.