  Monopartite projections of bipartite networks are useful tools for modeling
indirect interactions in complex systems. The standard approach to identify
significant links is statistical validation using a suitable null network
model, such as the popular configuration model (CM) that constrains node
degrees and randomizes everything else. However different CM formulations
exist, depending on how the constraints are imposed and for which sets of
nodes. Here we systematically investigate the application of these formulations
in validating the same network, showing that they lead to different results
even when the same significance threshold is used. Instead a much better
agreement is obtained for the same density of validated links. We thus propose
a meta-validation approach that allows to identify model-specific significance
thresholds for which the signal is strongest, and at the same time to obtain
results independent of the way in which the null hypothesis is formulated. We
illustrate this procedure using data on scientific production of world
countries.
