[statnet_help] Convergence assessment in small networks/with limited data

David Kretschmer dkretsch at mail.uni-mannheim.de
Fri Oct 2 03:33:07 PDT 2020

Dear all,

I estimate models on relatively small networks (about 20-30 nodes), including dyadic covariates with limited information, e.g. with only one or two actual tie observations for the dyadic covariate in some of the networks.

I wonder about convergence analysis in this setup, in particular when considering density plots.

The values for the network statistics related to the dyadic covariate produced during ERGM estimation have to be zero or positive; they cannot be negative. Because the number of tie observations in the empirical network is so low (only one or two instances), the deviations from these observations shown in the convergence analysis very frequently cannot produce symmetrical density plots: Because the number predicted in the ERGM estimation is constrained to be zero or higher, the deviations are also constrained in one direction. This is also what I observe when looking at density plots for these networks.

What I would like to know is what this implies *substantively* for the analysis of these networks: Should criteria for convergence be relaxed in such settings, i.e., is it also fine for the density plots to be asymmetric? Or does this simply mean that results from these networks should not be interpreted at all?

Any help would be greatly appreciated.


David Kretschmer
Universität Mannheim
Mannheimer Zentrum für Europäische Sozialforschung (MZES)
A5, 6
68159 Mannheim
Tel.: +49-621-181-2024

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