时序多层网络熵值结构洞节点重要性建模  

Modeling of node importance in entropy-value structured hole of temporal multilayer network

作　　者：胡钢 牛琼[1,2] 王琴 许丽鹏 任勇军 HU Gang;NIU Qiong;WANG Qin;XU Li-peng;REN Yong-jun(Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes,Anhui University of Technology,Maanshan 243032,China;School of Management Science and Engineering,Anhui University of Technology,Maanshan 243032,China;School of Computer Science,Nanjing University of Information Science and Technology,Nanjing 210044,China)

机构地区：[1]安徽工业大学、复杂系统多学科管理与控制安徽普通高校重点实验室,安徽马鞍山243032 [2]安徽工业大学管理科学与工程学院,安徽马鞍山243032 [3]南京信息工程大学计算机与软件学院,江苏南京210044

出　　处：《浙江大学学报（工学版）》2023年第4期719-725,734,共8页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(71772002);安徽省自然科学基金资助项目(2108085MG236);安徽省高校自然科学研究资助项目(KJ2021A0385);安徽省高校研究生科学研究资助项目(YJS20210356);安徽普通高校重点实验室开放基金资助项目(GS2021-05)。

摘　　要：分析动态多层复杂网络时空演化过程中的网络节点重要性序结构,提出时序多层网络熵值结构洞节点重要性辨识模型.分析时序网络节点局部信息熵的属性与节点全局K-shell信息集结偏好信息熵.依据复杂网络结构洞系数,提出节点熵值结构洞节点重要性辨识模型.时序化处理节点演化信息,提出节点重要性时序网络计算模型.通过SIR模型检验节点传播效率,开展实证网络仿真.本文的时序多层网络节点演化重要性排序结果与经典时序网络模型相比,Kendall值有了显著的提高.The importance ranking structure of network nodes in the temporal and spatial evolution process of temporal multilayer network was analyzed.A model for identifying the importance of network nodes in the temporal evolution process of temporal multilayer networks based on entropy-value structured holes was proposed.The attributes of local information entropy of nodes in the temporal network and their global K-shell information aggregation preference entropy were analyzed.A model for identifying the importance of nodes of their entropy and structural hole was proposed based on the complex network structural hole coefficient.A temporal network calculation model was developed for analyzing the temporal evolution of node importance.The efficiency of node propagation was tested by using the SIR model,and empirical network simulations were conducted.The simulation results showed a significant improvement in the Kendall value of the temporal evolution ranking of network nodes compared to classic temporal network models.

关 键 词：熵值 结构洞 K壳 时序网络 节点重要性 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]