  Many systems of scientific interest can be conceptualized as multipartite
networks. Examples include the spread of sexually transmitted infections,
scientific collaborations, human friendships, product recommendation systems,
and metabolic networks. In practice, these systems are often studied after
projection onto a single class of nodes, losing crucial information. Here, we
address a significant knowledge gap by comparing transmission dynamics on
temporal multipartite networks and on their time-aggregated unipartite
projections to determine the impact of the lost information on our ability to
predict the systems' dynamics. We show that the dynamics of transmission models
can be dramatically dissimilar on multipartite networks and on their
projections at three levels: final outcome, the magnitude of the variability
from realization to realization, and overall shape of the temporal trajectory.
We find that the ratio of the number of nodes to the number of active edges
over the time aggregation scale determines the ability of projected networks to
capture the dynamics on the multipartite network. Finally, we explore which
properties of a multipartite network are crucial in generating synthetic
networks that better reproduce the dynamical behavior observed in real
multipartite networks.
