IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1007434.html
   My bibliography  Save this article

Nine quick tips for analyzing network data

Author

Listed:
  • Vincent Miele
  • Catherine Matias
  • Stéphane Robin
  • Stéphane Dray

Abstract

No abstract is available for this item.

Suggested Citation

  • Vincent Miele & Catherine Matias & Stéphane Robin & Stéphane Dray, 2019. "Nine quick tips for analyzing network data," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-10, December.
  • Handle: RePEc:plo:pcbi00:1007434
    DOI: 10.1371/journal.pcbi.1007434
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007434
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007434&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1007434?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
    ---><---

    References listed on IDEAS

    as
    1. Phillip P. A. Staniczenko & Jason C. Kopp & Stefano Allesina, 2013. "The ghost of nestedness in ecological networks," Nature Communications, Nature, vol. 4(1), pages 1-6, June.
    2. Bo Wang & Armin Pourshafeie & Marinka Zitnik & Junjie Zhu & Carlos D. Bustamante & Serafim Batzoglou & Jure Leskovec, 2018. "Network enhancement as a general method to denoise weighted biological networks," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    3. Catherine Matias & Vincent Miele, 2017. "Statistical clustering of temporal networks through a dynamic stochastic block model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1119-1141, September.
    4. Marinka Zitnik & Rok Sosič & Jure Leskovec, 2018. "Prioritizing network communities," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    5. Richard F. Betzel & John D. Medaglia & Danielle S. Bassett, 2018. "Diversity of meso-scale architecture in human and non-human connectomes," Nature Communications, Nature, vol. 9(1), pages 1-14, December.
    6. Robert E Kass & Brian S Caffo & Marie Davidian & Xiao-Li Meng & Bin Yu & Nancy Reid, 2016. "Ten Simple Rules for Effective Statistical Practice," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-8, June.
    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. Cañizares, Jessica R. & Reed, J. Michael & Fefferman, Nina H., 2024. "Network theory and migration: Avoiding misapplications and misinterpretations," Ecological Modelling, Elsevier, vol. 496(C).
    2. Andrea Schaller & Gabriele Fohr & Carina Hoffmann & Gerrit Stassen & Bert Droste-Franke, 2021. "Supporting Cross-Company Networks in Workplace Health Promotion through Social Network Analysis—Description of the Methodological Approach and First Results from a Model Project on Physical Activity P," IJERPH, MDPI, vol. 18(13), pages 1-15, June.

    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. Jiang, Binyan & Li, Jialiang & Yao, Qiwei, 2023. "Autoregressive networks," LSE Research Online Documents on Economics 119983, London School of Economics and Political Science, LSE Library.
    2. Fabio Saracco & Riccardo Di Clemente & Andrea Gabrielli & Tiziano Squartini, 2015. "Detecting early signs of the 2007-2008 crisis in the world trade," Papers 1508.03533, arXiv.org, revised Jul 2016.
    3. Thorben Funke & Till Becker, 2019. "Stochastic block models: A comparison of variants and inference methods," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-40, April.
    4. Ludkin, Matthew, 2020. "Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    5. Cameron Mura & Mike Chalupa & Abigail M Newbury & Jack Chalupa & Philip E Bourne, 2020. "Ten simple rules for starting research in your late teens," PLOS Computational Biology, Public Library of Science, vol. 16(11), pages 1-11, November.
    6. Jun Liu & Jiangzhou Wang & Binghui Liu, 2020. "Community Detection of Multi-Layer Attributed Networks via Penalized Alternating Factorization," Mathematics, MDPI, vol. 8(2), pages 1-20, February.
    7. Markovič, Rene & Gosak, Marko & Grubelnik, Vladimir & Marhl, Marko & Virtič, Peter, 2019. "Data-driven classification of residential energy consumption patterns by means of functional connectivity networks," Applied Energy, Elsevier, vol. 242(C), pages 506-515.
    8. Zhiwei Yang & Weigang Wu & Yishun Chen & Xiaola Lin & Jiannong Cao, 2018. "(Q, S)-distance model and counting algorithms in dynamic distributed systems," International Journal of Distributed Sensor Networks, , vol. 14(1), pages 15501477187, January.
    9. Majid Noroozi & Marianna Pensky, 2022. "The Hierarchy of Block Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 64-107, June.
    10. Andrew C Murphy & Sarah F Muldoon & David Baker & Adam Lastowka & Brittany Bennett & Muzhi Yang & Danielle S Bassett, 2018. "Structure, function, and control of the human musculoskeletal network," PLOS Biology, Public Library of Science, vol. 16(1), pages 1-27, January.
    11. Li Guo & Wolfgang Karl Härdle & Yubo Tao, 2024. "A Time-Varying Network for Cryptocurrencies," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(2), pages 437-456, April.
    12. Paul Riverain & Simon Fossier & Mohamed Nadif, 2023. "Poisson degree corrected dynamic stochastic block model," 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. 17(1), pages 135-162, March.
    13. Lorenzo Federico & Ayoub Mounim & Pierpaolo D’Urso & Livia De Giovanni, 2024. "Complex networks and deep learning for copper flow across countries," Annals of Operations Research, Springer, vol. 339(1), pages 937-963, August.
    14. Luiz G. A. Alves & Giuseppe Mangioni & Isabella Cingolani & Francisco A. Rodrigues & Pietro Panzarasa & Yamir Moreno, 2018. "The nested structural organization of the worldwide trade multi-layer network," Papers 1803.02872, arXiv.org, revised Sep 2019.
    15. Wang, Zhixiao & Rui, Xiaobin & Yuan, Guan & Cui, Jingjing & Hadzibeganovic, Tarik, 2021. "Endemic information-contagion outbreaks in complex networks with potential spreaders based recurrent-state transmission dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    16. Tuure Hameri & Marc-Olivier Boldi & Vassily Hatzimanikatis, 2019. "Statistical inference in ensemble modeling of cellular metabolism," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-16, December.
    17. Tad Dallas & Andrew W Park & John M Drake, 2017. "Predicting cryptic links in host-parasite networks," PLOS Computational Biology, Public Library of Science, vol. 13(5), pages 1-15, May.
    18. Dragana M. Pavlović & Bryan R.L. Guillaume & Soroosh Afyouni & Thomas E. Nichols, 2020. "Multi‐subject stochastic blockmodels with mixed effects for adaptive analysis of individual differences in human brain network cluster structure," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 363-396, August.
    19. Bartolucci, Francesco & Marino, Maria Francesca & Pandolfi, Silvia, 2018. "Dealing with reciprocity in dynamic stochastic block models," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 86-100.
    20. Chabert-Liddell, Saint-Clair & Barbillon, Pierre & Donnet, Sophie & Lazega, Emmanuel, 2021. "A stochastic block model approach for the analysis of multilevel networks: An application to the sociology of organizations," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).

    More about this item

    Statistics

    Access and download statistics

    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:plo:pcbi00:1007434. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.