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 and fragmenting an old network to create a new one. Typical examples include the rise of scientific ideas or abrupt changes in social media, like the rise of Facebook to the detriment of Myspace. 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.
- Complex systems