Multilevel network data facilitate statistical inference for curved ERGMs with geometrically weighted terms

Jonathan Stewart, Michael Schweinberger, Michal Bojanowski, Martina Morris

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Multilevel network data provide two important benefits for ERG modeling. First, they facilitate estimation of the decay parameters in geometrically weighted terms for degree and triad distributions. Estimating decay parameters from a single network is challenging, so in practice they are typically fixed rather than estimated. Multilevel network data overcome that challenge by leveraging replication. Second, such data make it possible to assess out-of-sample performance using traditional cross-validation techniques. We demonstrate these benefits by using a multilevel network sample of classroom networks from Poland. We show that estimating the decay parameters improves in-sample performance of the model and that the out-of-sample performance of our best model is strong, suggesting that our findings can be generalized to the population of interest.

Original languageEnglish (US)
Pages (from-to)98-119
Number of pages22
JournalSocial Networks
Volume59
DOIs
StatePublished - Oct 2019

All Science Journal Classification (ASJC) codes

  • Anthropology
  • Sociology and Political Science
  • General Social Sciences
  • General Psychology

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