  In community detection, datasets often suffer a sampling bias for which nodes
which would normally have a high affinity appear to have zero affinity. This
happens for example when two affine users of a social network were not exposed
to one another. Community detection on this kind of data suffers then from
considering affine nodes as not affine. To solve this problem, we explicitly
model the (non-)exposure mechanism in a Bayesian community detection framework,
by introducing a set of additional hidden variables. Compared to approaches
which do not model exposure, our method is able to better reconstruct the input
graph, while maintaining a similar performance in recovering communities.
Importantly, it allows to estimate the probability that two nodes have been
exposed, a possibility not available with standard models.
