IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i7p1087-d1369925.html
   My bibliography  Save this article

Semi-Local Integration Measure for Directed Graphs

Author

Listed:
  • Tajana Ban Kirigin

    (Faculty of Mathematics, University of Rijeka, Radmile Matejčić 2, 51000 Rijeka, Croatia
    These authors contributed equally to this work.)

  • Sanda Bujačić Babić

    (Faculty of Mathematics, University of Rijeka, Radmile Matejčić 2, 51000 Rijeka, Croatia
    These authors contributed equally to this work.)

Abstract

Directed and weighted graphs can be used for many real-world applications to model and analyse the quality and structure of communication within the system, the distribution and flow of information, and various resources, dependencies, resilience, etc. On social media platforms, for example, highly networked members, so-called influencers, disseminate information, opinions and trends to their followers, who in turn increase the popularity of the influencers through likes and comments. Both types of interaction have a major influence on discussions and activities in the social network. To identify the nodes with the highest integration and interconnectivity within the neighbourhood subnetwork, we introduce the Directed Semi-Local Integration ( D S L I ) centrality measure for directed and weighted graphs. This centrality measure evaluates the integration of nodes assessed by the presence of connection, the strength of links, the organisation and optimisation of inbound and outbound interconnectivity, and the redundancy in the local subnetwork, and provides a stronger differentiation of the importance of nodes than standard centrality measures. Thus, D S L I has the potential to be used for analysing the degree of integration for the uptake and dissemination of resources in complex networks in many different contexts.

Suggested Citation

  • Tajana Ban Kirigin & Sanda Bujačić Babić, 2024. "Semi-Local Integration Measure for Directed Graphs," Mathematics, MDPI, vol. 12(7), pages 1-17, April.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:7:p:1087-:d:1369925
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/7/1087/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/7/1087/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Marchiori, Massimo & Latora, Vito, 2000. "Harmony in the small-world," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 285(3), pages 539-546.
    3. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    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. Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Shokrollahi, Arman, 2015. "Improving detection of influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 833-845.
    2. Wu, Tao & Xian, Xingping & Zhong, Linfeng & Xiong, Xi & Stanley, H. Eugene, 2018. "Power iteration ranking via hybrid diffusion for vital nodes identification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 802-815.
    3. Li, Hanwen & Shang, Qiuyan & Deng, Yong, 2021. "A generalized gravity model for influential spreaders identification in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    4. Shugang Li & Ziming Wang & Beiyan Zhang & Boyi Zhu & Zhifang Wen & Zhaoxu Yu, 2022. "The Research of “Products Rapidly Attracting Users” Based on the Fully Integrated Link Prediction Algorithm," Mathematics, MDPI, vol. 10(14), pages 1-19, July.
    5. Yu, Senbin & Gao, Liang & Xu, Lida & Gao, Zi-You, 2019. "Identifying influential spreaders based on indirect spreading in neighborhood," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 418-425.
    6. 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.
    7. Zhang, Yin-Ting & Zhou, Wei-Xing, 2023. "Quantifying the status of economies in international crop trade networks: A correlation structure analysis of various node-ranking metrics," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    8. Gao, Shuai & Ma, Jun & Chen, Zhumin & Wang, Guanghui & Xing, Changming, 2014. "Ranking the spreading ability of nodes in complex networks based on local structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 130-147.
    9. Xie, Zheng & Lv, Yiqin & Song, Yiping & Wang, Qi, 2024. "Data labeling through the centralities of co-reference networks improves the classification accuracy of scientific papers," Journal of Informetrics, Elsevier, vol. 18(2).
    10. Huang, Wencheng & Li, Haoran & Yin, Yanhui & Zhang, Zhi & Xie, Anhao & Zhang, Yin & Cheng, Guo, 2024. "Node importance identification of unweighted urban rail transit network: An Adjacency Information Entropy based approach," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    11. Gao, Cai & Wei, Daijun & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2013. "A modified evidential methodology of identifying influential nodes in weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5490-5500.
    12. 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.
    13. Zhai, Li & Yan, Xiangbin & Zhang, Guojing, 2018. "Bi-directional h-index: A new measure of node centrality in weighted and directed networks," Journal of Informetrics, Elsevier, vol. 12(1), pages 299-314.
    14. Wang, Xiaojie & Su, Yanyuan & Zhao, Chengli & Yi, Dongyun, 2016. "Effective identification of multiple influential spreaders by DegreePunishment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 238-247.
    15. Wu, Tao & Chen, Leiting & Zhong, Linfeng & Xian, Xingping, 2017. "Enhanced collective influence: A paradigm to optimize network disruption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 43-52.
    16. Cai Gao & Xin Lan & Xiaoge Zhang & Yong Deng, 2013. "A Bio-Inspired Methodology of Identifying Influential Nodes in Complex Networks," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-11, June.
    17. Yang, Pingle & Meng, Fanyuan & Zhao, Laijun & Zhou, Lixin, 2023. "AOGC: An improved gravity centrality based on an adaptive truncation radius and omni-channel paths for identifying key nodes in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    18. Wei, Bo & Liu, Jie & Wei, Daijun & Gao, Cai & Deng, Yong, 2015. "Weighted k-shell decomposition for complex networks based on potential edge weights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 277-283.
    19. Thomas J. Sargent & John Stachurski, 2022. "Economic Networks: Theory and Computation," Papers 2203.11972, arXiv.org, revised Jul 2022.
    20. Berahmand, Kamal & Bouyer, Asgarali & Samadi, Negin, 2018. "A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 41-54.

    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:gam:jmathe:v:12:y:2024:i:7:p:1087-:d:1369925. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.