  Unreliable network data can cause community-detection methods to overfit and
highlight spurious structures with misleading information about the
organization and function of complex systems. Here we show how to detect
significant flow-based communities in sparse networks with missing links using
the map equation. Since the map equation builds on Shannon entropy estimation,
it assumes complete data such that analyzing undersampled networks can lead to
overfitting. To overcome this problem, we incorporate a Bayesian approach with
assumptions about network uncertainties into the map equation framework.
Results in both synthetic and real-world networks show that the Bayesian
estimate of the map equation provides a principled approach to revealing
significant structures in undersampled networks.
