An Auxiliary Learning Task-Enhanced Graph Convolutional Network Model for Highly-accurate Node Classification on Weakly Supervised Graphs

Zengmei Zhuo, Xin Luo, Meng Chu Zhou

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

1 Scopus citations

Abstract

Graph Convolutional Networks (GCNs) play a vital role in graph learning tasks such as semi-supervised learning. However, a GCN model requires a large amount of labeled data for verification and model selection, and learning on sparse labeled graphs is still a challenging issue. In order to solve this problem, this paper propose an auxiliary learning task enhanced graph convolutional network (A-GCN), which combines the target supervised learning task of the GCN model with the auxiliary unsupervised learning task to correct its network’s learning. The experimental results demonstrate that A-GCN can achieve a significant performance improvement compared with state-of-the-art methods on a weakly supervised graph.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE International Conference on Smart Data Services, SMDS 2021
EditorsNimanthi Atukorala, Carl K. Chang, Ernesto Damiani, Min Fu Lizhi, George Spanoudakis, Mudhakar Srivatsa, Zhongjie Wang, Jia Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages192-197
Number of pages6
ISBN (Electronic)9781665400589
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Smart Data Services, SMDS 2021 - Virtual, Online, United States
Duration: Sep 5 2021Sep 11 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Smart Data Services, SMDS 2021

Conference

Conference2021 IEEE International Conference on Smart Data Services, SMDS 2021
Country/TerritoryUnited States
CityVirtual, Online
Period9/5/219/11/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

Keywords

  • Auxiliary Learning Task
  • Graph Convolutional Network
  • Weakly Supervised Graph

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