  We propose a Bayesian methodology for one-mode projecting a bipartite network
that is being observed across a series of discrete time steps. The resulting
one mode network captures the uncertainty over the presence/absence of each
link and provides a probability distribution over its possible weight values.
Additionally, the incorporation of prior knowledge over previous states makes
the resulting network less sensitive to noise and missing observations that
usually take place during the data collection process. The methodology consists
of computationally inexpensive update rules and is scalable to large problems,
via an appropriate distributed implementation.
