  Researchers use community-detection algorithms to reveal large-scale
organization in biological and social networks, but community detection is
useful only if the communities are significant and not a result of noisy data.
To assess the statistical significance of the network communities, or the
robustness of the detected structure, one approach is to perturb the network
structure by removing links and measure how much the communities change.
However, perturbing sparse networks is challenging because they are inherently
sensitive; they shatter easily if links are removed. Here we propose a simple
method to perturb sparse networks and assess the significance of their
communities. We generate resampled networks by adding extra links based on
local information, then we aggregate the information from multiple resampled
networks to find a coarse-grained description of significant clusters. In
addition to testing our method on benchmark networks, we use our method on the
sparse network of the European Court of Justice (ECJ) case law, to detect
significant and insignificant areas of law. We use our significance analysis to
draw a map of the ECJ case law network that reveals the relations between the
areas of law.
