IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v85y2020i3d10.1007_s11336-020-09720-7.html
   My bibliography  Save this article

Exponential-Family Random Graph Models for Multi-Layer Networks

Author

Listed:
  • Pavel N. Krivitsky

    (The University of New South Wales)

  • Laura M. Koehly

    (National Institutes of Health)

  • Christopher Steven Marcum

    (National Institutes of Health)

Abstract

Multi-layer networks arise when more than one type of relation is observed on a common set of actors. Modeling such networks within the exponential-family random graph (ERG) framework has been previously limited to special cases and, in particular, to dependence arising from just two layers. Extensions to ERGMs are introduced to address these limitations: Conway–Maxwell–Binomial distribution to model the marginal dependence among multiple layers; a “layer logic” language to translate familiar ERGM effects to substantively meaningful interactions of observed layers; and nondegenerate triadic and degree effects. The developments are demonstrated on two previously published datasets.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:psycho:v:85:y:2020:i:3:d:10.1007_s11336-020-09720-7
    DOI: 10.1007/s11336-020-09720-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11336-020-09720-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11336-020-09720-7?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. Jeub, Lucas G. S. & Mahoney, Michael W. & Mucha, Peter J. & Porter, Mason A., 2017. "A local perspective on community structure in multilayer networks," Network Science, Cambridge University Press, vol. 5(2), pages 144-163, June.
    2. Magnani, Matteo & Wasserman, Stanley, 2017. "Introduction to the special issue on multilayer networks," Network Science, Cambridge University Press, vol. 5(2), pages 141-143, June.
    3. 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).
    4. Stanley Wasserman, 1987. "Conformity of two sociometric relations," Psychometrika, Springer;The Psychometric Society, vol. 52(1), pages 3-18, March.
    5. Julianne Holt-Lunstad & Timothy B Smith & J Bradley Layton, 2010. "Social Relationships and Mortality Risk: A Meta-analytic Review," PLOS Medicine, Public Library of Science, vol. 7(7), pages 1-1, July.
    6. Yue Ma & De Liu, 2017. "Introduction to the special issue on Crowdfunding and FinTech," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-4, December.
    7. Pierre Barbillon & Sophie Donnet & Emmanuel Lazega & Avner Bar-Hen, 2017. "Stochastic block models for multiplex networks: an application to a multilevel network of researchers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 295-314, January.
    8. 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.
    9. 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. Termeh Shafie & David Schoch, 2021. "Multiplexity analysis of networks using multigraph representations," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1425-1444, December.

    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. 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).
    2. 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.
    3. Termeh Shafie & David Schoch, 2021. "Multiplexity analysis of networks using multigraph representations," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1425-1444, December.
    4. Duxbury, Scott W, 2019. "Mediation and Moderation in Statistical Network Models," SocArXiv 9bs4u, Center for Open Science.
    5. 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.
    6. De Nicola, Giacomo & Fritz, Cornelius & Mehrl, Marius & Kauermann, Göran, 2023. "Dependence matters: Statistical models to identify the drivers of tie formation in economic networks," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 351-363.
    7. 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).
    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. Lee, Jihui & Li, Gen & Wilson, James D., 2020. "Varying-coefficient models for dynamic networks," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    10. Javier Sánchez García & Salvador Cruz Rambaud, 2024. "The network econometrics of financial concentration," Review of Managerial Science, Springer, vol. 18(7), pages 2007-2045, July.
    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. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    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:spr:psycho:v:85:y:2020:i:3:d:10.1007_s11336-020-09720-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.