Influencer identification of dynamical networks based on information entropy dimension reduction method

Abstract

Abstract Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control. Traditional methods usually started from the centrality, node location or the impact on the largest connected component after node destruction based mainly on network structure. However, these algorithms lack consideration for network state changes. We applied a model that combines random connectivity matrix and minimal low-dimensional structures to represent network connectivity. With mean field theory and information entropy to calculate node activity, we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes, and ultimately ranked them by importance. We apply this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C.elegans neural network. We found the critical entropy value of the network state changes and through the proposed method, we can calculate the nodes that indirectly affect muscle cells by neural layers.