  Community detection helps us simplify the complex configuration of networks,
but communities are reliable only if they are statistically significant. To
detect statistically significant communities, a common approach is to resample
the original network and analyze the communities. But resampling assumes
independence between samples, while the components of a network are inherently
dependent. Therefore, we must understand how breaking dependencies between
resampled components affects the results of the significance analysis. Here we
use scientific communication as a model system to analyze this effect. Our
dataset includes citations among articles published in journals in the years
1984-2010. We compare parametric resampling of citations with non-parametric
article resampling. While citation resampling breaks link dependencies, article
resampling maintains such dependencies. We find that citation resampling
underestimates the variance of link weights. Moreover, this underestimation
explains most of the differences in the significance analysis of ranking and
clustering. Therefore, when only link weights are available and article
resampling is not an option, we suggest a simple parametric resampling scheme
that generates link-weight variances close to the link-weight variances of
article resampling. Nevertheless, when we highlight and summarize important
structural changes in science, the more dependencies we can maintain in the
resampling scheme, the earlier we can predict structural change.
