{"title":"Current Situation and Application of Graph Data Mining Technology","authors":"Meng Zhang, Pingping Wei, Suzhi Zhang, Jiaxing Xu","doi":"10.14257/ijdta.2017.10.3.01","DOIUrl":null,"url":null,"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.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"32 1","pages":"1-10"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of database theory and application","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14257/ijdta.2017.10.3.01","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.