[1]李桃陶,周斌,王忠振. 基于社交网络的图数据挖掘应用研究[J].计算机技术与发展,2014,24(10):6-11.
　LI Tao-tao,ZHOU Bin,WANG Zhong-zhen. Research on Graph Data Mining Application Based on Social Network[J].,2014,24(10):6-11.
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 基于社交网络的图数据挖掘应用研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年10期
页码:
6-11
栏目:
智能、算法、系统工程
出版日期:
2014-10-10

文章信息/Info

Title:
 Research on Graph Data Mining Application Based on Social Network
文章编号:
1673-629X（2014）10-0006-06
作者:
 李桃陶; 周斌; 王忠振
 国防科学技术大学 计算机学院
Author(s):
 LI Tao-tao; ZHOU Bin; WANG Zhong-zhen
关键词:
 ; 图挖掘; 图查询; 图分类; 图聚类; 图形数据库; 社交网络
Keywords:
 ; graph mining; graph query; graph classification; graph clustering; graph database; social network
分类号:
TP39
文献标志码:
A
摘要:
 社交网络数据的高度复杂性给数据挖掘研究带来了巨大的挑战，而社交网络数据挖掘更注重实体之间相互关联的特点，使得图数据挖掘技术的研究与应用逐渐成为该领域的热点。传统数据挖掘，如聚类、分类、频繁模式挖掘等技术逐渐拓展到图数据挖掘领域。文中首先介绍了现阶段图数据挖掘算法(其中包括图查询、图聚类、图分类和图的频繁子图挖掘)的研究内容和存在的问题；其次介绍了图形数据库研究现状，以及对比了主流图形数据库管理系统的优劣；最后介绍了图挖掘技术在社交网络中的应用。
Abstract:
 The high complexity of the social network data brings a huge challenge for data mining research. The social network data min-ing pays more attention to the relationship between entities,and the research and application of graph mining technology is gradually be-coming a hotspot in the field. Traditional data mining,such as clustering,classification,frequent pattern mining technology has gradually extended to graph mining field. In this paper,introduce the present the research content of graph data mining algorithm ( including graph query,graph clustering,graph classification and frequent sub-graph mining) and the existing problems first. Second introduce the research status of graph database,and compare the advantages and disadvantages of the mainstream graphics database management system. Finally introduce the application of graph mining technology in social network.

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更新日期/Last Update: 2015-04-02