介度中心和PageRank算法应用场景分析  

Application Scenarios Analysis of Betweenness Centraliy and PageRank Algorithms

作　　者：王敏 曹宝香[1] 王蕾[2] 冯晓兵[2] 

机构地区：[1]曲阜师范大学信息科学与工程学院,山东日照276800 [2]中国科学院计算技术研究所计算机体系结构国家重点实验室,北京100190

出　　处：《计算机工程》2015年第12期299-304,共6页Computer Engineering

基　　金：国家"973"计划基金资助项目(2011CB302504);国家"863"计划基金资助项目(2012AA010902;2015AA011505);国家自然科学基金资助项目(61402445)

摘　　要：在复杂网络中,一般按照需求选取合适的关键点发现算法,常用的关键点发现算法分别是介度中心算法和PageRank算法。为了在特定应用场景下选取合适的关键点发现算法,选择7种不同类型网络下的16个真实数据集,分析比较介度中心算法和PageRank算法在这些数据集上得到的关键点集合的差异,总结出2种关键点发现算法的应用场景。实验结果表明,介度中心算法适用于对整个网络影响力较大的关键点应用场景,PageRank算法适用于某个领域内熟知度较高的关键点应用场景。To find the key vertex in the discovery of complex network has important significance in practical applications,the most commonly used algorithms are the key points found in the betweenness centrality algorithm and PageRank algorithm.It chooses the suitable algorithm based on need.The development of network is diversity,and it is important to choose the suitable algorithm in specific application.This paper tests and analyzes 16 graphs under 7 type networks.It is found that these two algorithms have different application scenarios,the key mediator of the center point of general algorithm generally has an important impact on the whole network of points,while the impact of key points PageRank algorithm on the entire network is relatively small.

关 键 词：关键点 介度中心算法 PAGERANK算法 差异 领域 应用场景 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]