Multilevel network data facilitate statistical inference for curved ergms with geometrically weighted terms
Published in Social Networks, 2019
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-ofsample 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.
Keywords: multilevel network; social network; exponential-family random graph model; curved exponential-family random graph model.
Recommended citation:
Stewart, J., Schweinberger, M., Morris, M., and Bojanowski, M. "Multilevel network data facilitate statistical inference for curved ergms with geometrically weighted terms." Social Networks. 59 (2019), 98-119.