IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v628y2023ics0378437123007434.html
   My bibliography  Save this article

Graph regularization centrality

Author

Listed:
  • Col, Alcebiades Dal
  • Petronetto, Fabiano

Abstract

This paper introduces a novel centrality for the nodes of a graph. Our centrality is based on the graph regularization, a tool of graph signal processing theory. For this reason, it is called graph regularization centrality (GRC). In order to define the centrality of a node, a delta signal centered on this node is defined and a new smooth signal is generated by the graph regularization of the delta signal. The transformation of the delta signal into the new smooth signal strongly depends on the position of the node in the graph. Our centrality takes advantage of this feature to define a centrality for each node of the graph. Synthetic and real-world graphs are used to demonstrate the effectiveness of our centrality that combines local and global positioning of nodes in one measure. Furthermore, it is compared against classical centralities and graph Fourier transform centrality, which is also based on graph signal processing theory. We conclude with a discussion of the main features of GRC and a proposal for potential future work.

Suggested Citation

  • Col, Alcebiades Dal & Petronetto, Fabiano, 2023. "Graph regularization centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    • Handle: RePEc:eee:phsmap:v:628:y:2023:i:c:s0378437123007434
    DOI: 10.1016/j.physa.2023.129188
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437123007434
    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.2023.129188?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. Mussone, L. & Viseh, H. & Notari, R., 2022. "Novel centrality measures and applications to underground networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    2. Agryzkov, Taras & Tortosa, Leandro & Vicent, Jose F., 2019. "A variant of the current flow betweenness centrality and its application in urban networks," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 600-615.
    3. Curado, Manuel & Rodriguez, Rocio & Tortosa, Leandro & Vicent, Jose F., 2022. "Anew centrality measure in dense networks based on two-way random walk betweenness," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    4. Ni, Chengzhang & Yang, Jun & Kong, Demei, 2020. "Sequential seeding strategy for social influence diffusion with improved entropy-based centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    5. Hu, Ping & Fan, Wenli & Mei, Shengwei, 2015. "Identifying node importance in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 169-176.
    6. Singh, Rahul & Chakraborty, Abhishek & Manoj, B.S., 2017. "GFT centrality: A new node importance measure for complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 487(C), pages 185-195.
    7. Yang, Xu-Hua & Xiong, Zhen & Ma, Fangnan & Chen, Xiaoze & Ruan, Zhongyuan & Jiang, Peng & Xu, Xinli, 2021. "Identifying influential spreaders in complex networks based on network embedding and node local centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    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. Wei Wang & Dechao Ma & Fengzhi Wu & Mengxin Sun & Shuangqing Xu & Qiuyue Hua & Ziyuan Sun, 2023. "Exploring the Knowledge Structure and Hotspot Evolution of Greenwashing: A Visual Analysis Based on Bibliometrics," Sustainability, MDPI, vol. 15(3), pages 1-35, January.
    3. Karolina Dudzic-Gyurkovich, 2023. "Study of Centrality Measures in the Network of Green Spaces in the City of Krakow," Sustainability, MDPI, vol. 15(18), pages 1-30, September.
    4. Wen, Xiangxi & Tu, Congliang & Wu, Minggong, 2018. "Node importance evaluation in aviation network based on “No Return” node deletion method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 546-559.
    5. Hu, Ping & Fan, Wen-Li, 2020. "Mitigation strategy against cascading failures considering vulnerable transmission line in power grid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    6. Wang, Shuliang & Chen, Chen & Zhang, Jianhua & Gu, Xifeng & Huang, Xiaodi, 2022. "Vulnerability assessment of urban road traffic systems based on traffic flow," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).
    7. Bowater, David & Stefanakis, Emmanuel, 2023. "Extending the Adapted PageRank Algorithm centrality model for urban street networks using non-local random walks," Applied Mathematics and Computation, Elsevier, vol. 446(C).
    8. Shota Tabata, 2024. "A centrality measure for grid street network considering sequential route choice behaviour," Environment and Planning B, , vol. 51(3), pages 610-624, March.
    9. Liu, Wei & Song, Zhaoyang, 2020. "Review of studies on the resilience of urban critical infrastructure networks," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    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. Dong, Lijun & Wang, Yi & Liu, Ran & Pi, Benjie & Wu, Liuyi, 2016. "Toward edge minability for role mining in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 274-286.
    12. Martinez-Pastor, Beatriz & Nogal, Maria & O’Connor, Alan & Teixeira, Rui, 2022. "Identifying critical and vulnerable links: A new approach using the Fisher information matrix," International Journal of Critical Infrastructure Protection, Elsevier, vol. 39(C).
    13. Cao, Renmeng & Geng, Yu & Xu, Xiaoke & Wang, Xianwen, 2022. "How does duplicate tweeting boost social media exposure to scholarly articles?," Journal of Informetrics, Elsevier, vol. 16(1).
    14. Zhou, Bo & Song, Qiankun & Zhao, Zhenjiang & Liu, Tangzhi, 2020. "A reinforcement learning scheme for the equilibrium of the in-vehicle route choice problem based on congestion game," Applied Mathematics and Computation, Elsevier, vol. 371(C).
    15. Liang, Bo & Wang, Lin & Wang, Xiaofan, 2022. "OLMNE+FT: Multiplex network embedding based on overlapping links," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    16. Dong, Chen & Xu, Guiqiong & Meng, Lei & Yang, Pingle, 2022. "CPR-TOPSIS: A novel algorithm for finding influential nodes in complex networks based on communication probability and relative entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    17. Rocío Rodríguez & Manuel Curado & Francy D. Rodríguez & José F. Vicent, 2024. "Influential Yield Strength of Steel Materials with Return Random Walk Gravity Centrality," Mathematics, MDPI, vol. 12(3), pages 1-12, January.
    18. Zhu, Canshi & Wang, Xiaoyang & Zhu, Lin, 2017. "A novel method of evaluating key nodes in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 96(C), pages 43-50.
    19. Wang, Ning & Gao, Ying & He, Jia-tao & Yang, Jun, 2022. "Robustness evaluation of the air cargo network considering node importance and attack cost," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    20. Hu, Ping & Mei, Ting, 2018. "Ranking influential nodes in complex networks with structural holes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 624-631.

    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:628:y:2023:i:c:s0378437123007434. 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.