  Bipartite networks composed of dichotomous node sets are ubiquitous in nature
and society. Partly for simplicity's sake, many studies have focused on their
projection onto their unipartite versions where one only needs to care about a
single type of node. When it comes to mesoscale structures such as communities,
however, properly incorporating a priori structural restrictions such as
bipartivity is ever more important. In this paper, as a case study, we take the
community structure of bipartite networks in various scales to examine the
amount of information of bipartivity encoded in the community detection
procedure. In particular, we report the robustness in reliability of detected
community based on consistency by comparing the detection algorithm with or
without the consideration of bipartivity. From the analysis with model networks
embedding prescribed communities and real networks, we find that the community
detection tailored to take the bipartivity into account clearly yields more
robust community structures than the one without such structural information.
This demonstrates the necessity for customizing the community detection
algorithm by encoding whatever information is known about networks of interest
and, at the same time, raises an interesting question on the possibility of
estimating the quantitative amount of information from such a customization.
