首页 | 本学科首页   官方微博 | 高级检索  
     

图嵌入方法与应用:研究综述
引用本文:祁志卫,王笳辉,岳昆,乔少杰,李劲.图嵌入方法与应用:研究综述[J].电子学报,2020,48(4):808-818.
作者姓名:祁志卫  王笳辉  岳昆  乔少杰  李劲
作者单位:1. 云南大学信息学院, 云南昆明 650500; 2. 成都信息工程大学网络空间安全学院, 四川成都 610225; 3. 云南大学软件学院, 云南昆明 650500
摘    要:图模型越来越广泛地应用于数据管理、知识发现和信息服务等问题中,图嵌入作为图分析和应用的重要技术手段,成为了人工智能领域研究的热点之一.本文从图嵌入研究中面临的挑战出发,主要介绍了基于矩阵分解、基于随机游走和基于深度学习的图嵌入方法.接着,介绍了图嵌入方法常用的测试数据集、评测标准和典型应用.最后,总结了图嵌入未来研究的趋势和方向.

关 键 词:图模型  图嵌入方法  图嵌入应用  测试数据集  评测标准  
收稿时间:2018-12-17

Methods and Applications of Graph Embedding:A Survey
QI Zhi-wei,WANG Jia-hui,YUE Kun,QIAO Shao-jie,LI Jin.Methods and Applications of Graph Embedding:A Survey[J].Acta Electronica Sinica,2020,48(4):808-818.
Authors:QI Zhi-wei  WANG Jia-hui  YUE Kun  QIAO Shao-jie  LI Jin
Affiliation:1. School of Information Science and Engineering, Yunnan University, Kunming, Yunnan 650500, China; 2. School of Cybersecurity, Chengdu University of Information Technology, Chengdu, Sichuan 610225, China; 3. School of Software, Yunnan University, Kunming, Yunnan 650500, China
Abstract:Graphs are increasingly used in data management,knowledge discovery and information services.As an important strategy of graph analysis and applications,graph embedding has become one of the subjects with great attention in artificial intelligence.Starting from the challenges faced in graph embedding studies,this paper introduces the principal methods based on matrix decomposition,random walk and deep learning.Then,we introduce general test datasets,evaluation criteria as well as typical applications widely used in graph embedding.Finally,we summarize the trend and future research issues of graph embedding.
Keywords:graph model  graph embedding method  graph embedding application  test datasets  evaluation metrics  
点击此处可从《电子学报》浏览原始摘要信息
点击此处可从《电子学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号