摘要
传统不确定性复杂网络节点相似性的数理统计模型对于节点的统计效果较差,精确度较低,无法较为完整的再现网络结构。针对这一问题,构建了一种新的不确定性复杂网络节点相似性的数理统计模型。构建随机网络模型,固定网络节点数量,找寻节点关联关系,构建边迭代网络模型,最后依据找出的节点关系进行相似性的算法模型计算,实现对不确定性复杂网络节点相似性数理的统计。与传统统计模型进行对比实验研究,结果表明,新式不确定性复杂网络节点相似性的数理统计模型的统计效果优于传统统计模型。
The traditional mathematical and statistical model of node similarity in uncertain complex networks has poor statistical effect on nodes,low accuracy,and cannot reproduce the network structure completely. Aiming at this problem,a new mathematical statistical model of node similarity in uncertain complex networks is constructed. The random network model is built,the number of network nodes is fixed,the correlation relation of nodes is found,and the edge iteration network model is built. Finally,the similarity algorithm model is calculated based on the node relation found,so as to realize the mathematical statistics of the similarity of uncertain complex network nodes. Compared with the traditional statistical model,the results show that the statistical effect of the new uncertain complex network node similarity mathematical statistical model is better than the traditional statistical model.
作者
汪小黎
WANG Xiao-li(Shangluo College,Shangluo 726000,China)
出处
《电子设计工程》
2020年第3期89-92,共4页
Electronic Design Engineering
基金
商洛学院重点学科建设项目(201812)
陕西省教育厅专项科研计划项目(16JK1243)。
关键词
复杂网络
节点相似性
统计模型
模型构建
complex network
node similarity
statistical model
model construction
