  Many network contagion processes are inherently multiplex in nature, yet are
often reduced to processes on uniplex networks in analytic practice. We
therefore examine how data modeling choices can affect the predictions of
contagion processes. We demonstrate that multiplex contagion processes are not
simply the union of contagion processes over their constituent uniplex
networks. We use multiplex network data from two different contexts -- (1) a
behavioral network to represent their potential for infectious disease
transmission using a "simple" epidemiological model, and (2) users from online
social network sites to represent their potential for information spread using
a threshold-based "complex" contagion process. Our results show that contagion
on multiplex data is not captured accurately in models developed from the
uniplex networks even when they are combined, and that the nature of the
differences between the (combined) uniplex and multiplex results depends on the
specific spreading process over these networks.
