Social Network Analysis Based on Network Motifs

IF 1.2 Q2 MATHEMATICS, APPLIED Journal of Applied Mathematics Pub Date : 2014-02-16 DOI:10.1155/2014/874708
Xu Hong-lin, Yang Han-bing, Gao Cui-fang, Zhu Ping
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引用次数: 6

Abstract

Based on the community structure characteristics, theory, and methods of frequent subgraph mining, network motifs findings are firstly introduced into social network analysis; the tendentiousness evaluation function and the importance evaluation function are proposed for effectiveness assessment. Compared with the traditional way based on nodes centrality degree, the new approach can be used to analyze the properties of social network more fully and judge the roles of the nodes effectively. In application analysis, our approach is shown to be effective.
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基于网络母题的社会网络分析
基于频繁子图挖掘的社区结构特征、理论和方法,首次将网络母题的发现引入社会网络分析;提出了有效性评价的倾向性评价函数和重要性评价函数。与传统的基于节点中心性的方法相比,该方法可以更全面地分析社会网络的属性,有效地判断节点的作用。应用分析表明,该方法是有效的。
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来源期刊
Journal of Applied Mathematics
Journal of Applied Mathematics MATHEMATICS, APPLIED-
CiteScore
2.70
自引率
0.00%
发文量
58
审稿时长
3.2 months
期刊介绍: Journal of Applied Mathematics is a refereed journal devoted to the publication of original research papers and review articles in all areas of applied, computational, and industrial mathematics.
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