一种基于BBV网络的局部搜索策略

A local search strategy based on BBV networks

基于BBV网络,文章在对随机游走(Rand Walk,RW)策略、最大度搜索(High Degree Seeking,DS)和局部介数(Local Betweenness Centrality,LBC)等局部搜索策略进行对比研究的基础上,结合了搜索信息最小及局部中心节点的思想,提出一种结合节点聚类系数和边权值大小的搜索策略,即最小聚类系数最小距离(Minimum Clustering Coefficient and Distance,MCD)搜索策略。理论分析和仿真实验表明:在搜索代价、搜索时间及搜索覆盖率等方面,MCD搜索策略都优于DS策略和最大LBC搜索策略。

On the base of BBV networks,this article is trying to take a study on comparing several local search strategies such as Rand Walk strategy(RW),High Degree Seeking(DS),and Local Betweenness Centrality(LBC).By doing this comparison,Minimum Clustering Coefficient and Distance(MCD)algorithm is proposed on the base of the feature of BBV networks.A function with clustering coefficient of node and Weight is constructed through a comparative analysis of various search strategies.Meanwhile,Minimum Clustering Coefficient And Distance(MCD)algorithm which has combined the idea of searching the minimum and partial central part of information is proposed.It is concluded from theoretical analysis and numerical simulation that MCD performs better than DS and LBC on some important parameters,like the cost,coverage and time of searching.