IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v142y2020ics0167947319301720.html
   My bibliography  Save this article

A multilayer exponential random graph modelling approach for weighted networks

Author

Listed:
  • Caimo, Alberto
  • Gollini, Isabella

Abstract

A new modelling approach for the analysis of weighted networks with ordinal/ polytomous dyadic values is introduced. Specifically, it is proposed to model the weighted network connectivity structure using a hierarchical multilayer exponential random graph model (ERGM) generative process where each network layer represents a different ordinal dyadic category. The network layers are assumed to be generated by an ERGM process conditional on their closest lower network layers. A crucial advantage of the proposed method is the possibility of adopting the binary network statistics specification to describe both the between-layer and across-layer network processes and thus facilitating the interpretation of the parameter estimates associated to the network effects included in the model. The Bayesian approach provides a natural way to quantify the uncertainty associated to the model parameters. From a computational point of view, an extension of the approximate exchange algorithm is proposed to sample from the doubly-intractable parameter posterior distribution. A simulation study is carried out on artificial data and applications of the methodology are illustrated on well-known datasets. Finally, a goodness-of-fit diagnostic procedure for model assessment is proposed.

Suggested Citation

  • Caimo, Alberto & Gollini, Isabella, 2020. "A multilayer exponential random graph modelling approach for weighted networks," Computational Statistics & Data Analysis, Elsevier, vol. 142(C).
  • Handle: RePEc:eee:csdana:v:142:y:2020:i:c:s0167947319301720
    DOI: 10.1016/j.csda.2019.106825
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947319301720
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2019.106825?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yingjie Lu & Xinwei Wang & Lin Su & Han Zhao, 2023. "Multiplex Social Network Analysis to Understand the Social Engagement of Patients in Online Health Communities," Mathematics, MDPI, vol. 11(21), pages 1-20, October.
    2. Park, Jaewoo & Jin, Ick Hoon & Schweinberger, Michael, 2022. "Bayesian model selection for high-dimensional Ising models, with applications to educational data," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    3. Zhou Nie, 2023. "Using Exponential Random Graph Models for Social Networks to Understand Meta-Communication in Digital Media," Social Sciences, MDPI, vol. 12(4), pages 1-11, April.
    4. Polansky, Alan M. & Pramanik, Paramahansa, 2021. "A motif building process for simulating random networks," Computational Statistics & Data Analysis, Elsevier, vol. 162(C).
    5. 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).

    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:csdana:v:142:y:2020:i:c:s0167947319301720. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.elsevier.com/locate/csda .

    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.