  Contagion processes, representing the spread of infectious diseases,
information, or social behaviors, are often schematized as taking place on
networks, which encode for instance the interactions between individuals. The
impact of the network structure on spreading process has been widely
investigated, but not the reverse question: do different processes unfolding on
a given network lead to different infection patterns? How do the infection
patterns depend on a model's parameters or on the nature of the contagion
processes? Here we address this issue by investigating the infection patterns
for a variety of models. In simple contagion processes, where contagion events
involve one connection at a time, we find that the infection patterns are
extremely robust across models and parameters. In complex contagion models
instead, in which multiple interactions are needed for a contagion event,
non-trivial dependencies on models parameters emerge, as the infection pattern
depends on the interplay between pairwise and group contagions. In models
involving threshold mechanisms moreover, slight parameter changes can
significantly impact the spreading paths. Our results show that it is possible
to study crucial features of a spread from schematized models, and inform us on
the variations between spreading patterns in processes of different nature.
