IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v065i12.html
   My bibliography  Save this article

ergm.graphlets: A Package for ERG Modeling Based on Graphlet Statistics

Author

Listed:
  • Yaveroğlu, Ömer Nebil
  • Fitzhugh, Sean M.
  • Kurant, Maciej
  • Markopoulou, Athina
  • Butts, Carter T.
  • Pržulj, Nataša

Abstract

Exponential-family random graph models are probabilistic network models that are parametrized by sufficient statistics based on structural (i.e., graph-theoretic) properties. The ergm package for the R statistical computing environment is a collection of tools for the analysis of network data within an exponential-family random graph model framework. Many different network properties can be employed as sufficient statistics for exponential- family random graph models by using the model terms defined in the ergm package; this functionality can be expanded by the creation of packages that code for additional network statistics. Here, our focus is on the addition of statistics based on graphlets. Graphlets are classes of small, connected, induced subgraphs that can be used to describe the topological structure of a network. We introduce an R package called ergm.graphlets that enables the use of graphlet properties of a network within the ergm package of R. The ergm.graphlets package provides a complete list of model terms that allows to incorporate statistics of any 2-, 3-, 4- and 5-node graphlets into exponential-family random graph models. The new model terms of the ergm.graphlets package enable both exponential-family random graph modeling of global structural properties and investigation of relationships between node attributes (i.e., covariates) and local topologies around nodes.

Suggested Citation

  • 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).
    • Handle: RePEc:jss:jstsof:v:065:i12
    DOI: http://hdl.handle.net/10.18637/jss.v065.i12
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v065i12/v65i12.pdf
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v065i12/ergm.graphlets_1.0.3.tar.gz
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v065i12/v65i12.R
    Download Restriction: no
    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v065.i12?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. 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).
    2. 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).
    3. 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.
    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. Lienert, Jeffrey & Koehly, Laura & Reed-Tsochas, Felix & Marcum, Christopher Steven, 2017. "An efficient counting method for the colored triad census," SocArXiv rd6kw, Center for Open Science.

    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. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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.
    6. Youyi Bi & Yunjian Qiu & Zhenghui Sha & Mingxian Wang & Yan Fu & Noshir Contractor & Wei Chen, 2021. "Modeling Multi-Year Customers’ Considerations and Choices in China’s Auto Market Using Two-Stage Bipartite Network Analysis," Networks and Spatial Economics, Springer, vol. 21(2), pages 365-385, June.
    7. Duxbury, Scott W, 2019. "Mediation and Moderation in Statistical Network Models," SocArXiv 9bs4u, Center for Open Science.
    8. Cody J. Dey & James S. Quinn, 2014. "Individual attributes and self-organizational processes affect dominance network structure in pukeko," Behavioral Ecology, International Society for Behavioral Ecology, vol. 25(6), pages 1402-1408.
    9. repec:jss:jstsof:24:i09 is not listed on IDEAS
    10. 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.
    11. Claudio Pinto, 2017. "Perceived quality and formation of inter-regional networks of health care migration," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 7(3), pages 1-5.
    12. Lee, Jihui & Li, Gen & Wilson, James D., 2020. "Varying-coefficient models for dynamic networks," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    13. Pavel N. Krivitsky & Laura M. Koehly & Christopher Steven Marcum, 2020. "Exponential-Family Random Graph Models for Multi-Layer Networks," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 630-659, September.
    14. Anna Malinovskaya & Philipp Otto, 2021. "Online network monitoring," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1337-1364, December.
    15. 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.
    16. 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.
    17. 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).
    18. Ivan Cucco, 2014. "Network-based policies and innovation networks in two Italian regions: a comparison through a social selection model," STUDI ECONOMICI, FrancoAngeli Editore, vol. 2014(114), pages 78-96.
    19. 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.
    20. André Spithoven & Jef Vlegels & Walter Ysebaert, 2021. "Commercializing academic research: a social network approach exploring the role of regions and distance," The Journal of Technology Transfer, Springer, vol. 46(4), pages 1196-1231, August.
    21. Johannes Pol, 2019. "Introduction to Network Modeling Using Exponential Random Graph Models (ERGM): Theory and an Application Using R-Project," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 845-875, October.

    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:jss:jstsof:v:065:i12. 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: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    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.