IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v66y2017i3p481-500.html
   My bibliography  Save this article

Sharing social network data: differentially private estimation of exponential family random-graph models

Author

Listed:
  • Vishesh Karwa
  • Pavel N. Krivitsky
  • Aleksandra B. Slavković

Abstract

No abstract is available for this item.

Suggested Citation

  • Vishesh Karwa & Pavel N. Krivitsky & Aleksandra B. Slavković, 2017. "Sharing social network data: differentially private estimation of exponential family random-graph models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 481-500, April.
  • Handle: RePEc:bla:jorssc:v:66:y:2017:i:3:p:481-500
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/rssc.12185
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Patrick O. Perry & Patrick J. Wolfe, 2013. "Point process modelling for directed interaction networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(5), pages 821-849, November.
    2. Steven Goodreau & James Kitts & Martina Morris, 2009. "Birds of a feather, or friend of a friend? using exponential random graph models to investigate adolescent social networks," Demography, Springer;Population Association of America (PAA), vol. 46(1), pages 103-125, February.
    3. Michell, Lynn & Amos, Amanda, 1997. "Girls, pecking order and smoking," Social Science & Medicine, Elsevier, vol. 44(12), pages 1861-1869, June.
    4. Hunter, David R. & Handcock, Mark S. & Butts, Carter T. & Goodreau, Steven M. & Morris, Martina, 2008. "ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i03).
    5. Morris, Martina & Handcock, Mark S. & Hunter, David R., 2008. "Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i04).
    6. Wasserman, Larry & Zhou, Shuheng, 2010. "A Statistical Framework for Differential Privacy," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 375-389.
    7. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
    8. Hunter, David R. & Goodreau, Steven M. & Handcock, Mark S., 2008. "Goodness of Fit of Social Network Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 248-258, March.
    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. Ashish Arora & Michelle Gittelman & Sarah Kaplan & John Lynch & Will Mitchell & Nicolaj Siggelkow & Ji Youn (Rose) Kim & Michael Howard & Emily Cox Pahnke & Warren Boeker, 2016. "Understanding network formation in strategy research: Exponential random graph models," Strategic Management Journal, Wiley Blackwell, vol. 37(1), pages 22-44, January.
    2. Cornelius Fritz & Michael Lebacher & Göran Kauermann, 2020. "Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 275-299, August.
    3. Lee, Jihui & Li, Gen & Wilson, James D., 2020. "Varying-coefficient models for dynamic networks," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    4. Krivitsky, Pavel N., 2017. "Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 149-161.
    5. Duxbury, Scott W, 2019. "Mediation and Moderation in Statistical Network Models," SocArXiv 9bs4u, Center for Open Science.
    6. Goodreau, Steven M. & Handcock, Mark S. & Hunter, David R. & Butts, Carter T. & Morris, Martina, 2008. "A statnet Tutorial," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i09).
    7. Duncan A. Clark & Mark S. Handcock, 2022. "Comparing the real‐world performance of exponential‐family random graph models and latent order logistic models for social network analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 566-587, April.
    8. Angel Ortiz-Pelaez & Getaneh Ashenafi & Francois Roger & Agnes Waret-Szkuta, 2012. "Can Geographical Factors Determine the Choices of Farmers in the Ethiopian Highlands to Trade in Livestock Markets?," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-11, February.
    9. Peng, Tai-Quan, 2015. "Assortative mixing, preferential attachment, and triadic closure: A longitudinal study of tie-generative mechanisms in journal citation networks," Journal of Informetrics, Elsevier, vol. 9(2), pages 250-262.
    10. Anna Malinovskaya & Philipp Otto, 2021. "Online network monitoring," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1337-1364, December.
    11. Yaveroğlu, Ömer Nebil & Fitzhugh, Sean M. & Kurant, Maciej & Markopoulou, Athina & Butts, Carter T. & Pržulj, Nataša, 2015. "ergm.graphlets: A Package for ERG Modeling Based on Graphlet Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i12).
    12. Emily Casleton & Daniel J. Nordman & Mark S. Kaiser, 2022. "Modeling Transitivity in Local Structure Graph Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 389-417, June.
    13. Jeffrey A. Smith & Jessica Burow, 2020. "Using Ego Network Data to Inform Agent-based Models of Diffusion," Sociological Methods & Research, , vol. 49(4), pages 1018-1063, November.
    14. Federica Bianchi & Francesco Bartolucci & Stefano Peluso & Antonietta Mira, 2020. "Longitudinal networks of dyadic relationships using latent trajectories: evidence from the European interbank market," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 711-739, August.
    15. Kei, Yik Lun & Chen, Yanzhen & Madrid Padilla, Oscar Hernan, 2023. "A partially separable model for dynamic valued networks," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    16. Joshua Daniel Loyal & Yuguo Chen, 2020. "Statistical Network Analysis: A Review with Applications to the Coronavirus Disease 2019 Pandemic," International Statistical Review, International Statistical Institute, vol. 88(2), pages 419-440, August.
    17. He, Xi-jun & Dong, Yan-bo & Wu, Yu-ying & Jiang, Guo-rui & Zheng, Yao, 2019. "Factors affecting evolution of the interprovincial technology patent trade networks in China based on exponential random graph models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 443-457.
    18. Cimenler, Oguz & Reeves, Kingsley A. & Skvoretz, John, 2015. "An evaluation of collaborative research in a college of engineering," Journal of Informetrics, Elsevier, vol. 9(3), pages 577-590.
    19. 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.
    20. Fischer, Manuel, 2015. "Collaboration patterns, external shocks and uncertainty: Swiss nuclear energy politics before and after Fukushima," Energy Policy, Elsevier, vol. 86(C), pages 520-528.

    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:bla:jorssc:v:66:y:2017:i:3:p:481-500. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.