IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v483y2017icp156-170.html
   My bibliography  Save this article

Detecting community structure in networks via consensus dynamics and spatial transformation

Author

Listed:
  • Yang, Bo
  • He, He
  • Hu, Xiaoming

Abstract

We present a novel clustering algorithm for community detection, based on the dynamics towards consensus and spatial transformation. The community detection problem is translated to a clustering problem in the N-dimensional Euclidean space by three stages: (1) the dynamics running on a network is emulated to a procedure of gas diffusion in a finite space; (2) the pressure distribution vectors are used to describe the influence that each node exerts on the whole network; (3) the similarity measures between two nodes are quantified in the N-dimensional Euclidean space by k-Nearest Neighbors method. After such steps, we could merge clusters according to their similarity distances and show the community structure of a network by a hierarchical clustering tree. Tests on several benchmark networks are presented and the results show the effectiveness and reliability of our algorithm.

Suggested Citation

  • Yang, Bo & He, He & Hu, Xiaoming, 2017. "Detecting community structure in networks via consensus dynamics and spatial transformation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 156-170.
  • Handle: RePEc:eee:phsmap:v:483:y:2017:i:c:p:156-170
    DOI: 10.1016/j.physa.2017.04.098
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117304077
    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.2017.04.098?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. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
    2. He, He & Yang, Bo & Hu, Xiaoming, 2016. "Exploring community structure in networks by consensus dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 342-353.
    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. Tselykh, Alexander & Vasilev, Vladislav & Tselykh, Larisa, 2019. "Clustering method based on the elastic energy functional of directed signed weighted graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 392-407.
    2. Yang, Bo & Li, Xu & Liu, Xiangwei & He, He & Chen, Wei, 2019. "Alternating between consensus and leader selection reveals community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 693-706.

    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. Emerson, Isaac Arnold & Amala, Arumugam, 2017. "Protein contact maps: A binary depiction of protein 3D structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 782-791.
    2. Ruiz Vargas, E. & Mitchell, D.G.V. & Greening, S.G. & Wahl, L.M., 2014. "Topology of whole-brain functional MRI networks: Improving the truncated scale-free model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 151-158.
    3. Igor Belykh & Mateusz Bocian & Alan R. Champneys & Kevin Daley & Russell Jeter & John H. G. Macdonald & Allan McRobie, 2021. "Emergence of the London Millennium Bridge instability without synchronisation," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    4. 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.
    5. Zhang, Yun & Liu, Yongguo & Li, Jieting & Zhu, Jiajing & Yang, Changhong & Yang, Wen & Wen, Chuanbiao, 2020. "WOCDA: A whale optimization based community detection algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    6. Soh, Harold & Lim, Sonja & Zhang, Tianyou & Fu, Xiuju & Lee, Gary Kee Khoon & Hung, Terence Gih Guang & Di, Pan & Prakasam, Silvester & Wong, Limsoon, 2010. "Weighted complex network analysis of travel routes on the Singapore public transportation system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5852-5863.
    7. Wang, Qingyun & Duan, Zhisheng & Chen, Guanrong & Feng, Zhaosheng, 2008. "Synchronization in a class of weighted complex networks with coupling delays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(22), pages 5616-5622.
    8. He, He & Yang, Bo & Hu, Xiaoming, 2016. "Exploring community structure in networks by consensus dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 342-353.
    9. Wu, Tianyu & Huang, Xia & Chen, Xiangyong & Wang, Jing, 2020. "Sampled-data H∞ exponential synchronization for delayed semi-Markov jump CDNs: A looped-functional approach," Applied Mathematics and Computation, Elsevier, vol. 377(C).
    10. Yang, Hyeonchae & Jung, Woo-Sung, 2016. "Structural efficiency to manipulate public research institution networks," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 21-32.
    11. Zhu, Mixin & Zhou, Xiaojun, 2023. "Hybrid opportunistic maintenance policy for serial-parallel multi-station manufacturing systems with spare part overlap," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    12. Ye, Dan & Yang, Xiang & Su, Lei, 2017. "Fault-tolerant synchronization control for complex dynamical networks with semi-Markov jump topology," Applied Mathematics and Computation, Elsevier, vol. 312(C), pages 36-48.
    13. Dragicevic, Arnaud Z. & Sinclair-Desgagné, Bernard, 2013. "Sustainable network dynamics," Ecological Modelling, Elsevier, vol. 270(C), pages 43-53.
    14. Luo, Mengzhuo & Liu, Xinzhi & Zhong, Shouming & Cheng, Jun, 2018. "Synchronization of multi-stochastic-link complex networks via aperiodically intermittent control with two different switched periods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 20-38.
    15. Wei, Daijun & Deng, Xinyang & Zhang, Xiaoge & Deng, Yong & Mahadevan, Sankaran, 2013. "Identifying influential nodes in weighted networks based on evidence theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2564-2575.
    16. Yao, Jialing & Sun, Bingbin & Xi, lifeng, 2019. "Fractality of evolving self-similar networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 211-216.
    17. Sanghoon Lee & Wonjoon Kim, 2017. "The knowledge network dynamics in a mobile ecosystem: a patent citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 717-742, May.
    18. Jihui Han & Wei Li & Longfeng Zhao & Zhu Su & Yijiang Zou & Weibing Deng, 2017. "Community detection in dynamic networks via adaptive label propagation," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-16, November.
    19. Wang, Xinhe & Lu, Junwei & Wang, Zhen & Li, Yuxia, 2020. "Dynamics of discrete epidemic models on heterogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    20. Carayol, Nicolas & Roux, Pascale, 2009. "Knowledge flows and the geography of networks: A strategic model of small world formation," Journal of Economic Behavior & Organization, Elsevier, vol. 71(2), pages 414-427, August.

    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:483:y:2017:i:c:p:156-170. 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.