Attention Mechanism-based Vital Nodes Identification in Complex Networks
ZHANG Mingleia, SONG Yurongb, QU Hongboa
a. School of Computer Science; b. College of Automation and College of Artificial Intelligence, Nanjing University of Post and Telecommunications, Nanjing 210023, China
Abstract：This study aims to address the problem of vital nodes identification in complex networks using graph attention mechanism. This paper integrates both node′s virus transmissibility and structural impact, constructing training labels on the generated network to learn node importance through a graph attention network model. Experimental results demonstrate the excellence of this algorithm in two critical tasks: influence maximization and immune isolation.
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