Hernan Makse (@kcore_analytics) is Director of Complex Networks and Data Science Lab (Levich Insititute), Professor of Physics at the City College of New York and founder and CEO of Kcore Analytics. His work focuses on complex network and data science. Dr. Makse has a record of high-impact research in big-data analytics and complex networks ranging from social networks to the brain. He has developed optimization and machine learning algorithms to identify influencers and to understand opinion trends to effectively predict election outcomes from social media. He is currently developing the next-generation analytics tools to track the opinions of the electorate from social media outlets.
Identifying essential nodes in complex networks is a central problem for biological systems to social systems. We treat this problem in three paradigmatic cases: the brain, ecosystems and social networks. Mathematically, we find the set of influential nodes by optimizing the damage to the giant connected component with systematic inactivation of nodes. We then apply network theory and pharmacogenetic interventions in a rat brain to predict and target essential nodes responsible for global integration in a model of learning and memory. We find that the integration of the brain network is mediated by a set of weak nodes through optimization of influence in optimal percolation. Pharmacogenetic inhibitions confirm the theoretical predictions. We discuss the relevance of these influencers to ecological systems dominated by abrupt first order tipping points as well as connectomes with regularities.
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