ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (8): 1663-1673.doi: 10.7544/issn1000-1239.2020.20200202

所属专题: 2020数据挖掘与知识发现专题

• 人工智能 • 上一篇    下一篇

条件变分时序图自编码器

陈可佳1,2,鲁浩1,张嘉俊1   

  1. 1(南京邮电大学计算机学院 南京 210023);2(江苏省大数据安全与智能处理重点实验室(南京邮电大学) 南京 210023) (chenkj@njupt.edu.cn)
  • 出版日期: 2020-08-01
  • 基金资助: 
    国家自然科学基金项目(61772284)

Conditional Variational Time-Series Graph Auto-Encoder

Chen Kejia1,2, Lu Hao1, Zhang Jiajun1   

  1. 1(School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023);2(Jiangsu Key Laboratory of Big Data Security & Intelligent Processing(Nanjing University of Posts and Telecommunications), Nanjing 210023)
  • Online: 2020-08-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61772284).

摘要: 网络表示学习(也被称为图嵌入)是链接预测、节点分类、社区发现、图可视化等图任务的基础.现有大多数的图嵌入算法主要是针对静态图开发的,难以捕捉现实世界的网络随时间进化的动态特征.目前,针对动态网络表示学习方法的研究工作仍相对不足.提出了条件变分时序图自编码器(TS-CVGAE),可以同时学习动态网络的局部结构和随时间的演化模式.该方法首先改进了传统图卷积得到时序图卷积,并在条件变分自编码器的框架下使用时序图卷积对网络节点进行编码.训练结束后,条件变分自编码器的中间层就是最终的网络嵌入结果.实验结果表明,该方法在4个现实动态网络数据集上的链接预测表现均优于相关的静、动态网络表示学习方法.

关键词: 网络表示学习, 条件变分自编码器, 动态网络, 图卷积, 链接预测

Abstract: Network representation learning (also called graph embedding) is the basis for graph tasks such as link prediction, node classification, community discovery, and graph visualization. Most of the existing graph embedding algorithms are mainly developed for static graphs, which is difficult to capture the dynamic characteristics of the real-world networks that evolve over time. At present, research on dynamic network representation learning is still inadequate. This paper proposes a conditional variational time-series graph auto-encoder (TS-CVGAE), which can simultaneously learn the local structure and evolution pattern of a dynamic network. The model improves the traditional graph convolution to obtain time-series graph convolution and uses it to encode the network in the framework of conditional variational auto-encoder. After training, the middle layer of TS-CVGAE is the final network embedding. Experimental results show that the method performs better in link prediction task than the related static and dynamic network representation learning methods with all four real dynamic network datasets.

Key words: network representation learning, conditional variational auto-encoder, dynamic network, graph convolution, link prediction

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