  Collections of journal papers, often referred to as 'citation networks', can
be modeled as a collection of coupled bipartite networks which tend to exhibit
linear growth and preferential attachment as papers are added to the
collection. Assuming primary nodes in the first partition and secondary nodes
in the second partition, the basic bipartite Yule process assumes that as each
primary node is added to the network, it links to multiple secondary nodes, and
with probability, $\alpha$, each new link may connect to a newly appearing
secondary node. The number of links from a new primary node follows some
distribution that is a characteristic of the specific network. Links to
existing secondary nodes follow a preferential attachment rule. With
modifications to adapt to specific networks, bipartite Yule processes simulate
networks that can be validated against actual networks using a wide variety of
network metrics. The application of bipartite Yule processes to the simulation
of paper-reference networks and paper-author networks is demonstrated and
simulation results are shown to mimic networks from actual collections of
papers across several network metrics.
