  A fundamental problem in network science is to predict how certain
individuals are able to initiate new networks to spring up "new ideas".
Frequently, these changes in trends are triggered by a few innovators who
rapidly impose their ideas through "viral" influence spreading producing
cascades of followers fragmenting an old network to create a new one. Typical
examples include the raise of scientific ideas or abrupt changes in social
media, like the raise of Facebook.com to the detriment of Myspace.com. How this
process arises in practice has not been conclusively demonstrated. Here, we
show that a condition for sustaining a viral spreading process is the existence
of a multiplex correlated graph with hidden "influence links". Analytical
solutions predict percolation phase transitions, either abrupt or continuous,
where networks are disintegrated through viral cascades of followers as in
empirical data. Our modeling predicts the strict conditions to sustain a large
viral spreading via a scaling form of the local correlation function between
multilayers, which we also confirm empirically. Ultimately, the theory predicts
the conditions for viral cascading in a large class of multiplex networks
ranging from social to financial systems and markets.
