IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v522y2019icp205-214.html
   My bibliography  Save this article

Network community detection from the perspective of time series

Author

Listed:
  • Wang, Dong
  • Zhao, Yi

Abstract

We present a quasi-isometric mapping to transform complex networks into time series, which enables the network distance to be strictly preserved and allows to solve the network clustering problem from the perspective of its time series. In order to reconstruct the network distance characteristics exactly, we weight the network links in several ways and then convert the weighted networks into time series via classical multidimensional scaling (CMDS). Given such a transformation framework, we utilize the criterion of relative eigenvalue gap (REG) to estimate the number of communities of a network. Further, we enunciate that the distributions of two time series from two isomorphic networks are identical. We then apply the distance-based k-means algorithm to the generated time series to detect the community structures of complex networks with success. The results of diverse simulated and real networks demonstrate the superiority of quasi-isometry-based time series in network community detection.

Suggested Citation

  • Wang, Dong & Zhao, Yi, 2019. "Network community detection from the perspective of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 205-214.
  • Handle: RePEc:eee:phsmap:v:522:y:2019:i:c:p:205-214
    DOI: 10.1016/j.physa.2019.01.028
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119300287
    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.2019.01.028?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. Gao, Zhong-Ke & Cai, Qing & Yang, Yu-Xuan & Dang, Wei-Dong, 2017. "Time-dependent limited penetrable visibility graph analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 476(C), pages 43-48.
    2. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    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. Wang, Tao & Chen, Shanshan & Wang, Xiaoxia & Wang, Jinfang, 2020. "Label propagation algorithm based on node importance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    2. Boris Mirkin & Soroosh Shalileh, 2022. "Community Detection in Feature-Rich Networks Using Data Recovery Approach," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 432-462, November.

    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. Thiemo Fetzer & Samuel Marden, 2017. "Take What You Can: Property Rights, Contestability and Conflict," Economic Journal, Royal Economic Society, vol. 0(601), pages 757-783, May.
    2. Daniel Agness & Travis Baseler & Sylvain Chassang & Pascaline Dupas & Erik Snowberg, 2022. "Valuing the Time of the Self-Employed," Working Papers 2022-2, Princeton University. Economics Department..
    3. Khanh Duong, 2024. "Is meritocracy just? New evidence from Boolean analysis and Machine learning," Journal of Computational Social Science, Springer, vol. 7(2), pages 1795-1821, October.
    4. Batool, Fatima & Hennig, Christian, 2021. "Clustering with the Average Silhouette Width," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    5. Nicoleta Serban & Huijing Jiang, 2012. "Multilevel Functional Clustering Analysis," Biometrics, The International Biometric Society, vol. 68(3), pages 805-814, September.
    6. Orietta Nicolis & Jean Paul Maidana & Fabian Contreras & Danilo Leal, 2024. "Analyzing the Impact of COVID-19 on Economic Sustainability: A Clustering Approach," Sustainability, MDPI, vol. 16(4), pages 1-30, February.
    7. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    8. Yaeji Lim & Hee-Seok Oh & Ying Kuen Cheung, 2019. "Multiscale Clustering for Functional Data," Journal of Classification, Springer;The Classification Society, vol. 36(2), pages 368-391, July.
    9. Forzani, Liliana & Gieco, Antonella & Tolmasky, Carlos, 2017. "Likelihood ratio test for partial sphericity in high and ultra-high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 18-38.
    10. Yujia Li & Xiangrui Zeng & Chien‐Wei Lin & George C. Tseng, 2022. "Simultaneous estimation of cluster number and feature sparsity in high‐dimensional cluster analysis," Biometrics, The International Biometric Society, vol. 78(2), pages 574-585, June.
    11. Vojtech Blazek & Michal Petruzela & Tomas Vantuch & Zdenek Slanina & Stanislav Mišák & Wojciech Walendziuk, 2020. "The Estimation of the Influence of Household Appliances on the Power Quality in a Microgrid System," Energies, MDPI, vol. 13(17), pages 1-21, August.
    12. Andrew Clark & Alexander Mihailov & Michael Zargham, 2024. "Complex Systems Modeling of Community Inclusion Currencies," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1259-1294, August.
    13. Nicoleta Serban, 2008. "Estimating and clustering curves in the presence of heteroscedastic errors," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(7), pages 553-571.
    14. Caruso, Germán & Scartascini, Carlos & Tommasi, Mariano, 2015. "Are we all playing the same game? The economic effects of constitutions depend on the degree of institutionalization," European Journal of Political Economy, Elsevier, vol. 38(C), pages 212-228.
    15. Alessandro Crimi & Olivier Commowick & Adil Maarouf & Jean-Christophe Ferré & Elise Bannier & Ayman Tourbah & Isabelle Berry & Jean-Philippe Ranjeva & Gilles Edan & Christian Barillot, 2014. "Predictive Value of Imaging Markers at Multiple Sclerosis Disease Onset Based on Gadolinium- and USPIO-Enhanced MRI and Machine Learning," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-10, April.
    16. Mehmet Çağlar & Cem Gürler, 2022. "Sustainable Development Goals: A cluster analysis of worldwide countries," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 8593-8624, June.
    17. Elizabeth Tipton & Robert B. Olsen, "undated". "Enhancing the Generalizability of Impact Studies in Education," Mathematica Policy Research Reports 35d5625333dc480aba9765b3b, Mathematica Policy Research.
    18. Cyril Atkinson-Clement & Eléonore Pigalle, 2021. "What can we learn from Covid-19 pandemic’s impact on human behaviour? The case of France’s lockdown," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-12, December.
    19. Jelle R Dalenberg & Luca Nanetti & Remco J Renken & René A de Wijk & Gert J ter Horst, 2014. "Dealing with Consumer Differences in Liking during Repeated Exposure to Food; Typical Dynamics in Rating Behavior," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
    20. Daniel Lewis & Davide Melcangi & Laura Pilossoph, 2019. "Latent Heterogeneity in the Marginal Propensity to Consume," 2019 Meeting Papers 519, Society for Economic Dynamics.

    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:522:y:2019:i:c:p:205-214. 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.