计算机科学 ›› 2018, Vol. 45 ›› Issue (7): 38-41.doi: 10.11896/j.issn.1002-137X.2018.07.006

• 第五届CCF 大数据学术会议 • 上一篇    下一篇

一种多结构及文本融合的网络表征方法

李佳艺1,赵宇1,王莉2   

  1. 太原理工大学信息与计算机学院 山西 晋中0306001 ;
    太原理工大学大数据学院 山西 晋中0306002
  • 收稿日期:2017-07-22 出版日期:2018-07-30 发布日期:2018-07-30
  • 作者简介:李佳艺(1995-),女,硕士生,主要研究方向为社交网络、大数据挖掘,E-mail:1057339032@qq.com;赵 宇(1992-),女,硕士生,主要研究方向为社交网络、大数据挖掘,E-mail:411975417@qq.com;王 莉(1971-),女,博士,教授,CCF会员,主要研究方向为人工智能、大数据挖掘、社交网络,E-mail:462672475@qq.com(通信作者)。
  • 基金资助:
    本文受国家高技术研究发展计划863项目(2014AA015204),山西自然科学基金项目(201703D421013),中科院计算技术研究所网络数据科学重点实验室课题(CASNDST20140X)资助。

Network Representation Model Based on Multi-architectures and Text Fusion

LI Jia-yi1,ZHAO Yu1,WANG Li2   

  1. College of Information and Computer,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China1;
    College of Data Science,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China2
  • Received:2017-07-22 Online:2018-07-30 Published:2018-07-30

摘要: 网络表征通过对网络结构的深度学习得到节点的矢量表征,挖掘网络中潜在的信息,是社会计算中的一种重要降维方法。针对一种融合了网络中的文本和结构的、基于矩阵分解的网络表征方法TADW,首先分析并讨论了文本属性矩阵在矩阵分解式中的位置对网络表征效果的影响,并对此方法进行了优化;在此基础上,提出了一种融合关系结构、交互结构和文本属性的社交网络表征方法。在多个数据集上的实验结果表明,该方法在多分类任务中优于其他经典网络表征方法。

关键词: 表征学习, 多网络结构, 矩阵分解, 社交网络

Abstract: Network representation obtains the vector representations of nodes by deeply learning network structure,and mines the potential information on the network,which is an important method of reducing dimension in social computing.As for TADW,which is a network representation method based on matrix decomposition and combining text and structure,this paper first analyzed and discussed the influence of the location of text attributes matrix on network representation.Then,it proposed a social network representation method that incorporates relationship structure,interaction structure and textual attributes.Experimental results on multiple datasets show that the proposed method outperforms other classical network representation methods in classification tasks.

Key words: Matrix factorization, Multi-network structures, Representation learning, Social network

中图分类号: 

  • TP181
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