Node Sequencing and Visualization Model Construction of Information Propagation Temporal Network based on Interlayer Coupling Intensity Attenuation

Online social networks have become a significant medium for disseminating and acquiring information. This paper proposes a modeling method to mine important nodes in social networks using a super adjacency matrix temporal network based on the weakening of interactions between layers and the influence maximization algorithm of a temporal network. The centrality of eigenvectors was introduced to assess the importance of nodes, and the intensity of interlayer coupling was described using an attenuation factor. In addition, the calculation method of the propagation probability between nodes was also defined. The maximum connectivity components of the proposed model on the Enrons dataset were 0.744 and 0.7412 under different circumstances, and the maximum network performance changes were 0.229 and 0.02998. The maximum running times of the influence maximization algorithm under different conditions were 25.656 s and 58.302 s. The research results have practical significance in providing accurate advertising and information dissemination.