• 学术论文 •

### 基于元路径的动态异质网络表示学习

1. 重庆邮电大学计算机科学与技术学院，重庆 400065
• 收稿日期:2021-09-17 修回日期:2022-04-21 出版日期:2022-08-25
• 作者简介:
• 刘　群　女，1969年出生于江西省南昌市，博士，重庆邮电大学教授，博士生导师.主要研究方向为复杂网络、可解释人工智能和数据挖掘.E-mail： liuqun@cqupt.edu.cn
谭洪胜　男，1996年出生于重庆市.硕士生，主要研究方向为网络表示学习.E-mail： redtan2000@163.com
张优敏　女，1987年出生于陕西省兴平市.博士生，主要研究方向为图神经网络的可解释.E-mail： ymzhang0103@hotmail.com
王国胤　男，1970年出生于重庆市，博士，重庆邮电大学教授，博士生导师.教育部“长江学者”特聘教授（2015‒2019）、中组部“万人计划”科技创新领军人才（2014）、人社部“新世纪百千万人才工程”国家级人选、国务院特殊津贴专家、中科院“百人计划”专家、教育部“新世纪优秀人才”.主要研究方向为粗糙集、粒计算、数据挖掘、认知计算、大数据、人工智能等.E-mail： wanggy@cqupt.edu.cn
• 基金资助:
• 国家自然科学基金重点项目 (61936001); 重庆市教委重点合作项目 (HZ2021008); 重庆市自然科学基金 (cstc2021ycjh-bgzxm0013)

### Dynamic Heterogeneous Network Representation Method Based on Meta-Path

LIU Qun, TAN Hong-sheng, ZHANG You-min, WANG Guo-yin

1. College of Computer Science and Technology，Chongqing University of Posts and Telecommunications，Chongqing 400065，China
• Received:2021-09-17 Revised:2022-04-21 Online:2022-08-25 Published:2022-09-08

Abstract:

The researches of network representation learning have made many achievements. Since most of the researches ignore the dynamics and heterogeneity of the networks, coupled temporal and spatial structure features can not be distinguished, and rich semantic information of the network cannot be captured well. In this paper, meta-path based dynamic heterogeneous network representation learning method is proposed. Firstly, the neighborhood structures of nodes are divided into different sub-spaces according to their time, then the sequences of all time-weighted meta-paths for each node are sampled. Secondly, the neighborhood information on all time-weighted meta-paths of each node is integrated by a gated recurrent unit network(GRU). Furthermore, a bi-directional gated recurrent unit network(Bi-GRU) with an attention mechanism is used to learn the spatio-temporal contextual information from the merged sequences, and the final node representation will be received. Experiments on real data sets show that our algorithm has greatly improved performance on the downstream network tasks, such as node classification, clustering and visualization. Compared with state-of-the-art baseline methods,the Micro-F1 value has been raised by 1.09%~3.72% averagely on classification tasks, and the ARI value has been increased by 3.23%~14.49% on clustering tasks.