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blockmodeling: an R package for Generalized Blockmodeling

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Listed:
  • Matjašič, Miha
  • Cugmas, Marjan
  • Žiberna, Aleš

Abstract

This paper presents the R package blockmodeling which is primarily meant as an implementation of generalized blockmodeling (more broadly blockmodeling) for valued networks where the values of the ties are assumed to be measured on at least interval scale. Blockmodeling is one of the most commonly used approaches in the analysis of (social) networks, which deals with the analysis of relationships or connections, between the units studied (e.g., peoples, organizations, journals etc.). The R package blockmodeling implements several approaches for the generalized blockmodeling of binary and valued networks. Generalized blockmodeling is commonly used to cluster nodes in a network with regard to the structure of their links. The theoretical foundations of generalized blockmodeling for binary and valued networks are summarized in the paper while the use of the R package blockmodeling is illustrated by applying it to an empirical dataset.

Suggested Citation

  • Matjašič, Miha & Cugmas, Marjan & Žiberna, Aleš, 2021. "blockmodeling: an R package for Generalized Blockmodeling," SocArXiv b8cxp, Center for Open Science.
    • Handle: RePEc:osf:socarx:b8cxp
    DOI: 10.31219/osf.io/b8cxp
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    References listed on IDEAS

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    1. Marjan Cugmas & Anuška Ferligoj & Luka Kronegger, 2016. "The stability of co-authorship structures," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 163-186, January.
    2. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    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. Marjan Cugmas & Dawn DeLay & Aleš Žiberna & Anuška Ferligoj, 2020. "Symmetric core-cohesive blockmodel in preschool children’s interaction networks," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-19, January.
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