基于多层次灰色关联分析的复杂网络节点排序模型

复杂网络节点重要性是研究复杂网络特性的重要方面之一,被广泛应用于数据挖掘、Web搜索、社会网络分析等众多研究领域.在选取评估节点重要性指标时,考虑到普通聚类系数仅能衡量网络节点聚类的疏密度,不能衡量聚类的规模,提出了修正的聚类系数;同时,选取了Erdos数和介数两个指标来综合衡量网络节点重要性,建立多层次灰色关联分析模型,确定出各个节点与理想节点的关联度,实现对复杂网络节点的排序.模型不仅考虑到度、路径距离对节点排序的影响,而且也考虑到每个节点聚类程度对节点排序的影响.通过与实际网络和其他方法的排序结果对比,模型能够准确找到复杂网络的核心节点,并且排序结果真实反映了节点依次的重要程度....

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Bibliographic Details
Published in计算机工程与科学 Vol. 36; no. 6; pp. 1165 - 1171
Main Author 曹卫东 刘红霞
Format Journal Article
LanguageChinese
Published 中国民航大学信息科研基地,天津,300300 2014
Subjects
Erdos数
介数
修正的聚类系数
复杂网络
多层次灰色关联分析
节点重要性排序
多层次灰色关联分析
Erdos数
Erdos number
修正的聚类系数
nodes importance ranking
amended clustering coefficient
complex network
multi-level gray relational analysis
betweenness
介数
复杂网络
节点重要性排序
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ISSN1007-130X
DOI10.3969/j.issn.1007-130X.2014.06.027

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Summary:复杂网络节点重要性是研究复杂网络特性的重要方面之一,被广泛应用于数据挖掘、Web搜索、社会网络分析等众多研究领域.在选取评估节点重要性指标时,考虑到普通聚类系数仅能衡量网络节点聚类的疏密度,不能衡量聚类的规模,提出了修正的聚类系数;同时,选取了Erdos数和介数两个指标来综合衡量网络节点重要性,建立多层次灰色关联分析模型,确定出各个节点与理想节点的关联度,实现对复杂网络节点的排序.模型不仅考虑到度、路径距离对节点排序的影响,而且也考虑到每个节点聚类程度对节点排序的影响.通过与实际网络和其他方法的排序结果对比,模型能够准确找到复杂网络的核心节点,并且排序结果真实反映了节点依次的重要程度.
Bibliography:Nodes importance ranking in complex network is one of the most important aspects of studying the properties of complex network.It has been widely used in data mining,web search,analysis of social network and so on.When selecting the node importance evaluation index,considering that the average clustering coefficient can only measure a network node clustering density,and not measure the clustering size,the amended clustering coefficient is proposed.Meanwhile,the other two indexes,the Erdos number and the betweenness,are selected to evaluate node importance in networks.A multilevel gray relational analysis model is established to identify the correlation degree between node and the optimal node,and to sort nodes of complex networks.The model considers the node sorting influence factors involving not only the degree of nodes and the path distance but also the degree of node clustering.Compared with the real network and other methods of sorting results,the model can accurately find the core nodes of the complex n
ISSN:1007-130X
DOI:10.3969/j.issn.1007-130X.2014.06.027