基于垂直维序列动态时间规整方法的图相似度度量

doi:10.13229/j.cnki.jdxbgxb20170600

吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (4): 1199-1205.doi: 10.13229/j.cnki.jdxbgxb20170600

基于垂直维序列动态时间规整方法的图相似度度量

王旭^1,2, 欧阳继红^1,2, 陈桂芬³

1.吉林大学计算机科学与技术学院,长春130012;
2.吉林大学符号计算与知识工程教育部重点实验室,长春130012;
3.吉林农业大学信息技术学院,长春 130118

收稿日期:2017-05-12 出版日期:2018-07-01 发布日期:2018-07-01
通讯作者: 欧阳继红(1964-),女,教授,博士生导师.研究方向:数据挖掘,时空推理.E-mail:ouyj@jlu.edu.cn
作者简介:王旭(1982-),男,博士研究生.研究方向:数据挖掘,图挖掘.E-mail:xuwang10@mails.jlu.edu.cn
基金资助:
国家自然科学基金项目(61472157,61602204,61402195).

Measurement of graph similarity based on vertical dimension sequence dynamic time warping method

WANG Xu^1,2, OUYANG Ji-hong^1,2, CHEN Gui-fen³

1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;
3.College of Information Technology, Jilin Agricultural University, Changchun 130118, China

Received:2017-05-12 Online:2018-07-01 Published:2018-07-01

摘要/Abstract

摘要： 针对图相似度度量过程中复杂度高、信息缺失的问题,采用将图转换为广义树,将广义树表示为垂直维序列的方法,通过计算垂直维序列的距离度量图的相似度。该方法把度量图相似度的问题简化为计算垂直维序列距离的问题。垂直维序列不仅包含了顶点标号、入度和出度信息,而且体现了顶点的层次结构特性,保留了图中的路径信息。与现有方法相比,该方法在度量过程中考虑了更多的图信息,并将时间复杂度降至O(n²)。

关键词: 人工智能, 图相似度度量, 动态时间规整, 垂直维序列, 距离计算, 时间复杂度

Abstract: To solve the problems of high complexity and information loss in the process of measuring graph similarity, a method to calculate the distance of vertical dimensional sequences is proposed, which is used to measure graph similarity. Using this method, the graph is converted into the generalized tree, which is regarded as the vertical dimensional sequences. This method simplifies the graph similarity measurement to the distance calculation of the sequences. The vertical dimensional sequences not only obtain labels, in-degrees and out-degrees information of vertices, reflect level structural property of vertices, but also reserve the path information of graph. Compared with the existing graph similarity methods, this method involves more graph information in the process of graph similarity measurement, and decreases the time complexity to O(n²).

Key words: artificial intelligence, graph similarity measure, dynamic time warping, vertical dimensional sequences, distance calculating, time complexity

中图分类号:

TP18

王旭, 欧阳继红, 陈桂芬. 基于垂直维序列动态时间规整方法的图相似度度量[J]. 吉林大学学报(工学版), 2018, 48(4): 1199-1205.

WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Measurement of graph similarity based on vertical dimension sequence dynamic time warping method[J]. 吉林大学学报(工学版), 2018, 48(4): 1199-1205.

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基于垂直维序列动态时间规整方法的图相似度度量

Measurement of graph similarity based on vertical dimension sequence dynamic time warping method

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