HNECV: Heterogeneous Network Embedding via Cloud Model and Variational Inference

Ming Yuan, Qun Liu*, Guoyin Wang*, Yike Guo

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

3 Citations (Scopus)


Deep learning has been successfully used in heterogeneous network embedding. Although it shows excellent performance on preserving the structure and semantic characteristics of network while a large scale of training data is provided, it is still challenging to model complex structured representations that effectively perform on diverse network tasks. In this work, a new heterogeneous network embedding learning method is presented based on cloud model and variational inference, called HNECV. The model uses meta-path random walks to obtain structural information of original network which can capture abundant semantics of networks from different views. In addition, a novel framework is put forward to build an excellent embedding. We employ the forward cloud transformation algorithm to improve the sampling method of the variational autoencoder in its hidden space, and then a self-supervised learning module is constructed to guide the cluster of node vectors in the hidden space of variational autoencoder. Experimental results indicate that the proposed model can achieve better performance than those of state-of-the-art algorithms. Furthermore, HNECV shows better robustness and steadiness on different network tasks when different ratio of edges are disconnected at training.

Original languageEnglish
Title of host publicationArtificial Intelligence: First CAAI International Conference, CICAI 2021, Hangzhou, China, June 5–6, 2021, Proceedings, Part I
EditorsLu Fang, Yiran Chen, Guangtao Zhai, Jane Wang, Ruiping Wang, Weisheng Dong
PublisherSpringer Cham
Number of pages12
ISBN (Electronic)9783030930462
ISBN (Print)9783030930455
Publication statusPublished - 1 Jan 2022
Event1st CAAI International Conference on Artificial Intelligence, CICAI 2021 - Hangzhou, China
Duration: 5 Jun 20216 Jun 2021

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence
NameCICAI: CAAI International Conference on Artificial Intelligence


Conference1st CAAI International Conference on Artificial Intelligence, CICAI 2021
Internet address

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Cloud model
  • Heterogeneous network
  • Meta-path
  • Representation learning
  • Variational autoencoder


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