IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v338y2024i2d10.1007_s10479-023-05193-w.html
   My bibliography  Save this article

Liberté, Égalité, Fraternité: a power study in signed networks

Author

Listed:
  • Wilhelm Rödder

    (FernUniversität in Hagen)

  • Andreas Dellnitz

    (Leibniz FH – School of Business)

  • Elmar Reucher

    (Private Hochschule für Wirtschaft und Technik)

Abstract

Power in human societies is a central phenomenon. Even though, it took ages to understand it and – even more – to measure it. Only in the last decades attempts were made to model power relations and to assign respective power indices to actors in a network. The present work goes a step further. It measures power of actors and groups of actors in networks by means of conditional relations. In a probabilistic framework, such relations are specified as conditionals: Which actor receives power given that the adjacent actor has it, and which actor looses power given that the neighbour dominates. This pattern of power relations allows for an exact calculation of an actor’s and groups of actors’ power index. The new decision analytics tool for this is maximizing entropy for the whole net and evaluating each actor’s influence therein. The new concept is applied to a middle size Kronecker net of clans and subclans operating in a today’s society.

Suggested Citation

  • Wilhelm Rödder & Andreas Dellnitz & Elmar Reucher, 2024. "Liberté, Égalité, Fraternité: a power study in signed networks," Annals of Operations Research, Springer, vol. 338(2), pages 1083-1100, July.
  • Handle: RePEc:spr:annopr:v:338:y:2024:i:2:d:10.1007_s10479-023-05193-w
    DOI: 10.1007/s10479-023-05193-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-023-05193-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10479-023-05193-w?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. Yuanzhi Yang & Lei Yu & Xing Wang & Siyi Chen & You Chen & Yipeng Zhou, 2020. "A novel method to identify influential nodes in complex networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 31(02), pages 1-14, February.
    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. Chaharborj, Sarkhosh Seddighi & Nabi, Khondoker Nazmoon & Feng, Koo Lee & Chaharborj, Shahriar Seddighi & Phang, Pei See, 2022. "Controlling COVID-19 transmission with isolation of influential nodes," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    2. Wu, Yali & Dong, Ang & Ren, Yuanguang & Jiang, Qiaoyong, 2023. "Identify influential nodes in complex networks: A k-orders entropy-based method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    3. Liu, Jia-Bao & Zheng, Ya-Qian & Lee, Chien-Chiang, 2024. "Statistical analysis of the regional air quality index of Yangtze River Delta based on complex network theory," Applied Energy, Elsevier, vol. 357(C).
    4. 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).
    5. Xu, Guiqiong & Meng, Lei, 2023. "A novel algorithm for identifying influential nodes in complex networks based on local propagation probability model," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    6. Wang, Jinping & Sun, Shaowei, 2024. "Identifying influential nodes: A new method based on dynamic propagation probability model," Chaos, Solitons & Fractals, Elsevier, vol. 185(C).
    7. Hajarathaiah, Koduru & Enduri, Murali Krishna & Anamalamudi, Satish, 2022. "Efficient algorithm for finding the influential nodes using local relative change of average shortest path," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    8. 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).
    9. Du, Yuxian & Lin, Xi & Pan, Ye & Chen, Zhaoxin & Xia, Huan & Luo, Qian, 2023. "Identifying influential airports in airline network based on failure risk factors with TOPSIS," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    10. Xiao, Feng & Li, Jin & Wei, Bo, 2022. "Cascading failure analysis and critical node identification in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    11. Wang, Ying & Zheng, Yunan & Shi, Xuelei & Liu, Yiguang, 2022. "An effective heuristic clustering algorithm for mining multiple critical nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    12. Xinyu Huang & Dongming Chen & Dongqi Wang & Tao Ren, 2020. "MINE: Identifying Top- k Vital Nodes in Complex Networks via Maximum Influential Neighbors Expansion," Mathematics, MDPI, vol. 8(9), pages 1-25, August.
    13. Li, Qi & Cheng, Le & Wang, Wei & Li, Xianghua & Li, Shudong & Zhu, Peican, 2023. "Influence maximization through exploring structural information," Applied Mathematics and Computation, Elsevier, vol. 442(C).
    14. Zhang, Xian-Jie & Wang, Jing & Ma, Xiao-Jing & Ma, Chuang & Kan, Jia-Qian & Zhang, Hai-Feng, 2022. "Influence maximization in social networks with privacy protection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    15. Wang, Longjian & Zheng, Shaoya & Wang, Yonggang & Wang, Longfei, 2021. "Identification of critical nodes in multimodal transportation network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    16. Jin, Pengfei & Wang, Saige & Meng, Zheng & Chen, Bin, 2023. "China's lithium supply chains: Network evolution and resilience assessment," Resources Policy, Elsevier, vol. 87(PB).
    17. Wang, Yan & Li, Haozhan & Zhang, Ling & Zhao, Linlin & Li, Wanlan, 2022. "Identifying influential nodes in social networks: Centripetal centrality and seed exclusion approach," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    18. Wu, Zhaoyan, 2024. "Intermittent control for identifying network topology," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    19. Lei, Mingli & Cheong, Kang Hao, 2022. "Node influence ranking in complex networks: A local structure entropy approach," Chaos, Solitons & Fractals, Elsevier, vol. 160(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:spr:annopr:v:338:y:2024:i:2:d:10.1007_s10479-023-05193-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.