一种基于影响力预测的节点排序模型  

Nodes’Ranking Model Based on Influence Prediction

作　　者：段顺然 尹美娟[1] 刘粉林[1] 焦隆隆 于岚岚 DUAN Shunran;YIN Meijuan;LIU Fenlin;JIAO Longlong;YU Lanlan(School of Cyberspace Security,Information Engineering University,Zhengzhou 450001,China)

机构地区：[1]战略支援部队信息工程大学网络空间安全学院,郑州450001

出　　处：《计算机科学》2023年第3期155-163,共9页Computer Science

基　　金：国家自然科学基金(U1804263);中原科技创新领军人才计划(214200510019)。

摘　　要：节点影响力排序一直是复杂网络研究的热点问题。Susceptible-Infected-Recovered(SIR)模型是一种较为理想的节点影响力排序方法,业内常将其用于评价其他的节点影响力排序方法,但该方法时间复杂度较高,难以实际应用。文中提出一个基于sir值学习的节点影响力排序模型,模型综合节点的局部和全局结构信息描述节点特征,利用机器学习方法构建sir值学习模型,以构建的同等规模网络的节点特征和sir值对模型进行训练,训练后的模型能够基于节点特征预测节点的sir值,进而实现节点影响力排序。文中基于该模型实现了一个具体的节点影响力排序方法,并在真实数据集上进行了实验,结果表明,基于该模型得到的影响力排序结果,其准确性和单调性相比度中心性、Kshell、Weighted Kshell degree neighborhood等基于结构特征的方法均有所提升。The ranking of nodes’influence has always been a hot issue in the research area of complex networks.Susceptible-infected-recovered(SIR)model is an ideal nodes’influence ranking method,which is commonly used to evaluate other nodes’in-fluence ranking methods.But it is difficult to be applied in practice due to its high time complexity.This paper proposes a nodes’influence ranking model based on sir value learning.Both the local structure and global structure information of nodes are used as features in the model.The sir value learning model is constructed by means of a deep learning model,which is trained on nodes’features and sir data set in synthetic graphs with the same size.The trained model can predict sir value based on nodes’features,and then rank nodes’influence based on predicted sir.In this paper,a specific nodes’influence ranking method is implemented based on the proposed model,and experiments are carried out on five real networks to verify the effectiveness of the method.The results show that the accuracy and monotonicity of nodes’influence ranking results are improved compared with degree centrality,Kshell and Weighted Kshell degree neighborhood.

关 键 词：复杂网络 节点影响力 SIR 影响力排序 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]