Noise Reduction in Network Embedding

Cong Li, Donghai Guan*, Zhiyuan Cui, Weiwei Yuan, Asad Masood Khattak, Muhammad Fahim

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Network Embedding aims to learn latent representations and effectively preserves structure of network and information of vertices. Recently, networks with rich side information such as vertex’s label and links between vertices have attracted significant interest due to its wide applications such as node classification and link prediction. It’s well known that, in real world applications, network always contains mislabeled vertices and edges, which will cause the embedding preserves mistake information. However, current semi-supervised graph embedding algorithms assume the vertex label is ground-truth. Manually relabel all mislabeled vertices is always inapplicable, therefore, how to effective reduce noise so as to maximize the graph analysis task performance is extremely important. In this paper, we focus on reducing label noise ratio in dataset to obtain more reasonable embedding. We proposed two methods for any semi-supervised network embedding algorithm to tackle it: first approach uses a model to identify potential noise vertices and correct them, second approach uses two voting strategy to precisely relabel vertex. To the best of our knowledge, we are the first to tackle this issue in network embedding. Our experiments are conducted on three public data sets.

Original languageEnglish
Title of host publicationMachine Learning and Intelligent Communications - 4th International Conference, MLICOM 2019: Proceedings
EditorsXiangping Bryce Zhai, Bing Chen, Kun Zhu
Number of pages12
ISBN (Print)9783030323875
Publication statusPublished - 2019
Externally publishedYes
Event4th International Conference on Machine Learning and Intelligent Communications, MLICOM 2019 - Nanjing, China
Duration: 24 Aug 201925 Aug 2019

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume294 LNCIST
ISSN (Print)1867-8211


Conference4th International Conference on Machine Learning and Intelligent Communications, MLICOM 2019

Bibliographical note

Funding Information:
Acknowledgements. This research was supported by Natural Science Foundation of China (Grant no. 61572252, 61672284). Meanwhile, this research work was supported by Zayed University Research Cluster Award \# R18038.

Publisher Copyright:
© 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Copyright 2019 Elsevier B.V., All rights reserved.


  • Network embedding
  • Noise identification
  • Voting

ASJC Scopus subject areas

  • Computer Networks and Communications


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