基于相对熵的节点影响力测量方法  

Measuring Method of Node Influence Based on Relative Entropy

作　　者：陈俊华[1] 边宅安 李慧嘉[1] 关闰丹 CEHN Jun-hua;BIAN Zhai-an;LI Hui-jia;GUAN Run-dan(School of Management Science and Engineering,Central University of Finance and Economics,Beijing 100081,China)

机构地区：[1]中央财经大学管理科学与工程学院,北京100081

出　　处：《计算机科学》2018年第B11期292-298,共7页Computer Science

摘　　要：识别中心节点是复杂网络分析的一个关键问题。文中结合现有的中心性测度方法,提出了一种利用TOPSIS法的相对熵,以此识别网络中的中心节点。现有的中心性测度方法可以被看作在复杂网络中确定各节点属性的排名。因此,提出的方法可以利用各种中心性测度方法的优点,获得一个更优的排名结果。最后用数值实验验证了所提方法的有效性。Recognizing central nodes is a key problem in complex network analysis,this paper proposed a method of relative entropy using TOPSIS(Technique for Order Performance by Similarity to Ideal Solution)method to identify the influential node in the network.The existing central measure methods can be considered as determining the rank of each node attribute in a complex network.Therefore,the method proposed in this paper can use the advantages of various central measure methods to obtain a better ranking result.Finally,the validity of the proposed method was verified by numerical experiments.

关 键 词：复杂网络 中心节点 中心性测度 相对熵 TOPSIS法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]