IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i3p439-d1329299.html
   My bibliography  Save this article

Influential Yield Strength of Steel Materials with Return Random Walk Gravity Centrality

Author

Listed:
  • Rocío Rodríguez

    (Tidop Research Group, University of Salamanca, Patio de Escuelas 1, E-38008 Salamanca, Spain
    These authors contributed equally to this work.)

  • Manuel Curado

    (Department of Computer Science and Artificial Intelligence, University of Alicante, Campus de San Vicente, Ap. Correos 99, E-03080 Alicante, Spain
    These authors contributed equally to this work.)

  • Francy D. Rodríguez

    (Department of Computer, Catholic University of Ãvila, Calle Canteros, s/n, E-05005 Ãvila, Spain
    These authors contributed equally to this work.)

  • José F. Vicent

    (Department of Computer Science and Artificial Intelligence, University of Alicante, Campus de San Vicente, Ap. Correos 99, E-03080 Alicante, Spain
    These authors contributed equally to this work.)

Abstract

In complex networks, important nodes have a significant impact, both functional and structural. From the perspective of data flow pattern detection, the evaluation of the importance of a node in a network, taking into account the role it plays as a transition element in random paths between two other nodes, has important applications in many areas. Advances in complex networks and improved data generation are very important for the growth of computational materials science. The search for patterns of behavior of the elements that make up steels through complex networks can be very useful in understanding their mechanical properties. This work aims to study the influence of the connections between the elements of steel and the impact of these connections on their mechanical properties, more specifically on the yield strength. The patterns found in the results show the significance of the proposed approach for the development of new steel compositions.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:439-:d:1329299
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/3/439/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/3/439/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Flaviano Morone & Hernán A. Makse, 2015. "Correction: Corrigendum: Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 527(7579), pages 544-544, November.
    2. Flaviano Morone & Hernán A. Makse, 2015. "Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 524(7563), pages 65-68, August.
    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).
    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. 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.
    2. Fink, Christian G. & Fullin, Kelly & Gutierrez, Guillermo & Omodt, Nathan & Zinnecker, Sydney & Sprint, Gina & McCulloch, Sean, 2023. "A centrality measure for quantifying spread on weighted, directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    3. Fan, Dongming & Sun, Bo & Dui, Hongyan & Zhong, Jilong & Wang, Ziyao & Ren, Yi & Wang, Zili, 2022. "A modified connectivity link addition strategy to improve the resilience of multiplex networks against attacks," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    4. Wang, Jingjing & Xu, Shuqi & Mariani, Manuel S. & Lü, Linyuan, 2021. "The local structure of citation networks uncovers expert-selected milestone papers," Journal of Informetrics, Elsevier, vol. 15(4).
    5. Gangwal, Utkarsh & Singh, Mayank & Pandey, Pradumn Kumar & Kamboj, Deepak & Chatterjee, Samrat & Bhatia, Udit, 2022. "Identifying early-warning indicators of onset of sudden collapse in networked infrastructure systems against sequential disruptions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    6. Wandelt, Sebastian & Sun, Xiaoqian & Zhang, Anming, 2023. "Towards analyzing the robustness of the Integrated Global Transportation Network Abstraction (IGTNA)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    7. Yin, Haofei & Zhang, Aobo & Zeng, An, 2023. "Identifying hidden target nodes for spreading in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    8. Qi, Mingze & Tan, Suoyi & Chen, Peng & Duan, Xiaojun & Lu, Xin, 2023. "Efficient network intervention with sampling information," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    9. Ping Pei & Haihan Zhang & Huizhen Zhang & Chen Yang & Tianbo An, 2024. "Network Synchronization via Pinning Control from an Attacker-Defender Game Perspective," Mathematics, MDPI, vol. 12(12), pages 1-17, June.
    10. Li, Sheng & Liu, Wenwen & Wu, Ruizi & Li, Junli, 2023. "An adaptive attack model to network controllability," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    11. James Flamino & Alessandro Galeazzi & Stuart Feldman & Michael W. Macy & Brendan Cross & Zhenkun Zhou & Matteo Serafino & Alexandre Bovet & Hernán A. Makse & Boleslaw K. Szymanski, 2023. "Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections," Nature Human Behaviour, Nature, vol. 7(6), pages 904-916, June.
    12. 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).
    13. Li Zeng & Changjun Fan & Chao Chen, 2023. "Leveraging Minimum Nodes for Optimum Key Player Identification in Complex Networks: A Deep Reinforcement Learning Strategy with Structured Reward Shaping," Mathematics, MDPI, vol. 11(17), pages 1-13, August.
    14. Almeira, Nahuel & Perotti, Juan Ignacio & Chacoma, Andrés & Billoni, Orlando Vito, 2021. "Explosive dismantling of two-dimensional random lattices under betweenness centrality attacks," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    15. Tianle Pu & Li Zeng & Chao Chen, 2024. "Deep Reinforcement Learning for Network Dismantling: A K-Core Based Approach," Mathematics, MDPI, vol. 12(8), pages 1-12, April.
    16. Wu, Rui-Jie & Kong, Yi-Xiu & Di, Zengru & Zhang, Yi-Cheng & Shi, Gui-Yuan, 2022. "Analytical solution to the k-core pruning process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    17. Wandelt, Sebastian & Lin, Wei & Sun, Xiaoqian & Zanin, Massimiliano, 2022. "From random failures to targeted attacks in network dismantling," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    18. Zhang, Dayong & Men, Hao & Zhang, Zhaoxin, 2024. "Assessing the stability of collaboration networks: A structural cohesion analysis perspective," Journal of Informetrics, Elsevier, vol. 18(1).
    19. Han, Jihui & Zhang, Ge & Dong, Gaogao & Zhao, Longfeng & Shi, Yuefeng & Zou, Yijiang, 2024. "Exact analysis of generalized degree-based percolation without memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 642(C).
    20. Sun, Peng Gang & Che, Wanping & Quan, Yining & Wang, Shuzhen & Miao, Qiguang, 2022. "Random networks are heterogeneous exhibiting a multi-scaling law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(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:gam:jmathe:v:12:y:2024:i:3:p:439-:d:1329299. 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.