Searching for super spreaders in networks: from Twitter to the brain

CCNY Data Science, Networks, and Biology Seminar

Time and place

3:30 PM on Wednesday, October 12th, 2016; NAC 7/219

Prof. Hernan Makse (CCNY Physics)

Abstract

The whole frame of interconnections in complex networks is hinged on a specific set of nodes, called influencers or superspreaders, much smaller than the total size, which if activated would cause the spread of information to the whole network; or, if deactivated would cause its catastrophic collapse. Localizing this minimal set of influencers is a crucial problem in network science. We provide the theoretical framework to identify superspreaders via optimal percolation theory by minimizing the energy of a many-body system given by the largest eigenvalue of the nonbacktracking matrix of the network. Big data analyses, from Twitter to the Brain, reveal that the set of superspreaders is much smaller than the one predicted by hubs and rich clubs. The model predicts the map of neural collective influencers (NCI) in the brain. Our results may allow for intervention protocols to control brain activity by targeting influential neural nodes predicted by network theory leading to applications of clinical interest.