Current Situation and Application of Graph Data Mining Technology

Meng Zhang, Pingping Wei, Suzhi Zhang, Jiaxing Xu
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Abstract

As an important data structure, graph can be used to describe the complex relationship among stuffs. With the setting up of social network, web network and other network in figure data, data mining technology has gradually become a hot research. Traditional data mining technology has been applied to the field of graph data mining constantly. Consequently the development of the graph data mining technology has been accelerated. This paper demonstrates the definition of graph data, and the current graph data mining algorithms which include graph classification, graph clustering, query graph, graph matching, graph of frequent subgraph mining, and graphic database development status. At last, what challenges graph mining technology confronts is illustrated in this paper.
图数据挖掘技术的现状及应用
图是一种重要的数据结构,可以用来描述事物之间的复杂关系。随着社交网络、web网络等网络在图形数据中的建立,数据挖掘技术逐渐成为研究的热点。传统的数据挖掘技术在图数据挖掘领域得到了不断的应用。从而加速了图数据挖掘技术的发展。本文阐述了图数据的定义,当前的图数据挖掘算法,包括图分类、图聚类、查询图、图匹配、频繁子图挖掘,以及图数据库的发展现状。最后,本文阐述了图挖掘技术面临的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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