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### 基于变分推断和元路径分解的异质网络表示方法

1. 重庆邮电大学 计算机科学与技术学院, 重庆 400065
• 收稿日期:2020-06-18 出版日期:2021-05-28 发布日期:2021-06-01
• 通讯作者: 刘群 E-mail:liuqun@cqupt.edu.cn
• 作者简介:袁 铭(1996-),男,重庆市人,硕士生,主要研究方向为网络表示学习.
• 基金资助:
国家自然科学基金重点项目(61936001);国家自然科学基金(61772096);国家重点研发计划(2016QY01W0200)

### A Heterogeneous Network Representation Method Based on Variational Inference and Meta-Path Decomposition

YUAN Ming, LIU Qun(), SUN Haichao, TAN Hongsheng

1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
• Received:2020-06-18 Online:2021-05-28 Published:2021-06-01
• Contact: LIU Qun E-mail:liuqun@cqupt.edu.cn

Abstract:

Aimed at the problem that the traditional meta-path random walk in heterogeneous network representation cannot accurately describe the heterogeneous network structure and cannot capture the true distribution of network nodes well, a heterogeneous network representation method based on variational inference and meta-path decomposition is proposed, which is named HetVAE. First, combining with the idea of path similarity, a node selection strategy is designed to improve the random walk of the meta-path. Next, the variational theory is introduced to effectively sample the latent variables in the original distribution. After that, a personalized attention machanism is implemented, which weights the node vector representation of different sub-networks obtained by decomposition. Then, these node vectors are fused by the proposed model, so that the final node vector representation can have richer semantic information. Finally, several experiments on different network tasks are performed on the three real data sets of DBLP, AMiner, and Yelp. The effectiveness of the model is verified by these results. In node classification and node clustering tasks, compared with some state-of-the-art algorithms, the Micro-F1 and normalized mutual information (NMI) increase by 1.12% to 4.36% and 1.35% to 18% respectively. It is proved that HetVAE can effectively capture the heterogeneous network structure and learn the node vetcor representation that conforms more with the true distribution.