Identification of Influential Spreaders in Complex Networks Using Neighbor Nodes' Entropy and Properties

Identifying the most influential individuals spreading ideas, information, or infectious diseases is a topic receiving significant attention from network researchers, since such identification can assist or hinder information dissemination, product exposure, and contagious disease detection. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, few efforts have been made to use node diversity within network structures to measure spreading ability. During the proposed one-year investigation the two-step framework described in this proposal will use a robust and reliable measure that combines neighbor nodes' entropy and topology properties to identify the most influential network nodes. Results from a series of Susceptible-Infected-Recovered (SIR) epidemic simulations will indicate that our proposal performs well and stably in single initial spreader scenarios associated with various complex network datasets.

Project ID:PB10708-2097
External Project ID:MOST107-2221-E182-069