Identifying multiple influential spreaders in complex networks based on spectral graph theory  

作　　者：崔东旭 何嘉林 肖子飞 任卫平 Dong-Xu Cui;Jia-Lin He;Zi-Fei Xiao;Wei-Ping Ren(School of Computer Science and Engineering,China West Normal University,Nanchong 637009,China)

机构地区：[1]School of Computer Science and Engineering,China West Normal University,Nanchong 637009,China

出　　处：《Chinese Physics B》2023年第9期603-610,共8页中国物理B（英文版）

基　　金：the National Natural Science Foundation of China(Grant No.62176217);the Program from the Sichuan Provincial Science and Technology,China(Grant No.2018RZ0081);the Fundamental Research Funds of China West Normal University(Grant No.17E063)。

摘　　要：One of the hot research topics in propagation dynamics is identifying a set of critical nodes that can influence maximization in a complex network.The importance and dispersion of critical nodes among them are both vital factors that can influence maximization.We therefore propose a multiple influential spreaders identification algorithm based on spectral graph theory.This algorithm first quantifies the role played by the local structure of nodes in the propagation process,then classifies the nodes based on the eigenvectors of the Laplace matrix,and finally selects a set of critical nodes by the constraint that nodes in the same class are not adjacent to each other while different classes of nodes can be adjacent to each other.Experimental results on real and synthetic networks show that our algorithm outperforms the state-of-the-art and classical algorithms in the SIR model.

关 键 词：spectral graph theory Laplace matrix influence maximization multiple influential spreaders 

分 类 号：O157.5[理学—数学]