IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v536y2019ics0378437119306399.html
   My bibliography  Save this article

Identifying similar networks using structural hierarchy

Author

Listed:
  • Saxena, Rakhi
  • Kaur, Sharanjit
  • Bhatnagar, Vasudha

Abstract

Comparing structural similarities among complex networks is an important task in several scientific and social science applications. Existing techniques for quantifying network similarity range from network-centric methods that consider global network topology to node-centric methods that consider local node-level sub-structures.

Suggested Citation

  • Saxena, Rakhi & Kaur, Sharanjit & Bhatnagar, Vasudha, 2019. "Identifying similar networks using structural hierarchy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    • Handle: RePEc:eee:phsmap:v:536:y:2019:i:c:s0378437119306399
    DOI: 10.1016/j.physa.2019.04.265
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119306399
    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.2019.04.265?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. Vladimir Batagelj & Matjaž Zaveršnik, 2011. "Fast algorithms for determining (generalized) core groups in social networks," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(2), pages 129-145, July.
    2. Traud, Amanda L. & Mucha, Peter J. & Porter, Mason A., 2012. "Social structure of Facebook networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4165-4180.
    3. Shi, Xiaolin & Adamic, Lada A. & Strauss, Martin J., 2007. "Networks of strong ties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(1), pages 33-47.
    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. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    2. Jiashun Jin & Zheng Tracy Ke & Shengming Luo, 2022. "Improvements on SCORE, Especially for Weak Signals," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 127-162, June.
    3. Han, Kevin & Basse, Guillaume & Bojinov, Iavor, 2024. "Population interference in panel experiments," Journal of Econometrics, Elsevier, vol. 238(1).
    4. Monika Cerinšek & Vladimir Batagelj, 2015. "Network analysis of Zentralblatt MATH data," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 977-1001, January.
    5. Ma, Shujie & Su, Liangjun & Zhang, Yichong, 2020. "Detecting Latent Communities in Network Formation Models," Economics and Statistics Working Papers 12-2020, Singapore Management University, School of Economics.
    6. Luca Braghieri & Ro'ee Levy & Alexey Makarin, 2022. "Social Media and Mental Health," American Economic Review, American Economic Association, vol. 112(11), pages 3660-3693, November.
    7. Ion Georgiou & Joaquim Heck & Andrej Mrvar, 2019. "The Analysis of Interconnected Decision Areas: A Computational Approach to Finding All Feasible Solutions," Group Decision and Negotiation, Springer, vol. 28(3), pages 543-563, June.
    8. Yuan, Wei-Guo & Liu, Yun, 2015. "A mixing evolution model for bidirectional microblog user networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 167-179.
    9. Karimi, Fariba & Ramenzoni, Verónica C. & Holme, Petter, 2014. "Structural differences between open and direct communication in an online community," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 263-273.
    10. Yakir Berchenko & Jonathan D. Rosenblatt & Simon D. W. Frost, 2017. "Modeling and analyzing respondent‐driven sampling as a counting process," Biometrics, The International Biometric Society, vol. 73(4), pages 1189-1198, December.
    11. Hanbaek Lyu & Yacoub H. Kureh & Joshua Vendrow & Mason A. Porter, 2024. "Learning low-rank latent mesoscale structures in networks," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    12. Drago, Carlo & Amidani Aliberti, Livia & Carbonai, Davide, 2014. "Measuring Gender Differences in Information Sharing Using Network Analysis: the Case of the Austrian Interlocking Directorship Network in 2009," Climate Change and Sustainable Development 178241, Fondazione Eni Enrico Mattei (FEEM).
    13. Yuan, Peiyan & Tang, Shaojie, 2015. "Community-based immunization in opportunistic social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 85-97.
    14. Marco Mancastroppa & Iacopo Iacopini & Giovanni Petri & Alain Barrat, 2023. "Hyper-cores promote localization and efficient seeding in higher-order processes," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    15. He, Dongxiao & Wang, Hongcui & Jin, Di & Liu, Baolin, 2016. "A model framework for the enhancement of community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 602-612.
    16. Huang, He & Yan, Zhijun & Pan, Yaohui, 2014. "Measuring edge importance to improve immunization performance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 532-540.
    17. Furini, Fabio & Ljubić, Ivana & Martin, Sébastien & San Segundo, Pablo, 2019. "The maximum clique interdiction problem," European Journal of Operational Research, Elsevier, vol. 277(1), pages 112-127.
    18. Yang, Xu-Hua & Chen, Guang & Chen, Sheng-Yong & Wang, Wan-Liang & Wang, Lei, 2014. "Study on some bus transport networks in China with considering spatial characteristics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 1-10.
    19. Wang, Benyu & Gu, Yijun & Zheng, Diwen, 2022. "Community detection in error-prone environments based on particle cooperation and competition with distance dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    20. Leifeld, Philip, 2018. "Polarization in the social sciences: Assortative mixing in social science collaboration networks is resilient to interventions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 510-523.

    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:536:y:2019:i:c:s0378437119306399. 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.