基于霍克斯点过程的动态网络表示学习方法

doi:10.3969/j.issn.0372-2112.2020.11.009

电子学报 ›› 2020, Vol. 48 ›› Issue (11): 2154-2161.DOI: 10.3969/j.issn.0372-2112.2020.11.009

基于霍克斯点过程的动态网络表示学习方法

尹赢, 张建朋, 吉立新, 李治成

战略支援部队信息工程大学信息技术研究所, 河南郑州 450003

收稿日期:2019-06-18 修回日期:2019-11-29 出版日期:2020-11-25
- 通讯作者:
- 张建朋
- 作者简介:
- 尹赢女,1994年出生,四川绵竹人.2017年毕业于西安交通大学电信学院,2017年进入国家数字交换系统工程技术研究中心.现为硕士研究生,主要从事网络表示学习的有关研究.E-mail:15883880517@163.com;吉立新 男,1969年出生,江苏淮安人.研究员.现为国家数字交换系统工程技术研究中心总工程师,主要研究方向为电信网分析.E-mail:jlxndsc@139.com;李治成 男,1996年出生,云南昌宁人.2018年毕业于四川大学计算机学院,2018年进入国家数字交换系统工程技术研究中心.现为硕士研究生,主要从事复杂网络相关研究.E-mail:lizc520a@gmail.com
- 基金资助:
- 国家自然科学基金创新研究群体资助项目 (No.61521003）

Dynamic Network Representation Learning Based on Hawkes Point Process

YIN Ying, ZHANG Jian-peng, JI Li-xin, LI Zhi-cheng

Institute of Information Technology, Information Engineering University, Zhengzhou, Henan 450003, China

Received:2019-06-18 Revised:2019-11-29 Online:2020-11-25 Published:2020-11-25

摘要/Abstract

摘要： 网络表示学习是将网络中的节点映射到低维空间形成低维稠密特征向量的分布式学习方法.本文在现有网络表示学习研究的基础上，提出一种基于霍克斯点过程的动态网络表示学习方法.该方法基于霍克斯点过程有效结合了网络历史连边信息和网络演化中的三元闭包特性对当前节点产生连边的影响，解决了现有方法难以有效捕捉网络历史信息和演化特性的问题.在多种数据集的实验结果表明，本文提出的方法较其它方法在节点分类、链路预测和可视化等实验中的性能均有较大的提高，实验中的F1分数值和AUC值分别提高了3.72%~6.41%和2.22%~4.69%.

关键词: 网络表示学习, 动态网络, 霍克斯点过程, 三元闭包理论

Abstract: Network representation learning is a distributed learning method that maps nodes in a network to low-dimensional spaces to form low-dimensional dense vectors.Based on the existing network representation learning research,this paper proposed a dynamic network representation learning method based on Hawkes point process,which effectively combines the network historical edges and the ternary closure characteristics in the network evolution to generate the new edges of the current nodes.It solves the problem that the existing methods are difficult to effectively capture the network historical information and evolution characteristics of dynamic networks.Extensive experiments demonstrated that the embeddings learned from the proposed MHNE (Multivariate Hawkes process Network Embedding) model can achieve better performance than the state-of-the-art methods in downstream tasks,such as node classification and link prediction.The F1 score and AUC value in the experiments increased by 3.72%~6.41% and 2.22%~4.69%,respectively.

Key words: network representation learning, dynamic network, Hawkes point process, ternary closure theory

中图分类号:

TP393

尹赢, 张建朋, 吉立新, 李治成. 基于霍克斯点过程的动态网络表示学习方法[J]. 电子学报, 2020, 48(11): 2154-2161.

YIN Ying, ZHANG Jian-peng, JI Li-xin, LI Zhi-cheng. Dynamic Network Representation Learning Based on Hawkes Point Process[J]. Acta Electronica Sinica, 2020, 48(11): 2154-2161.

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基于霍克斯点过程的动态网络表示学习方法

Dynamic Network Representation Learning Based on Hawkes Point Process

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