@inproceedings{f50a402cbf664763aef8c451f9106472,
title = "An Auxiliary Learning Task-Enhanced Graph Convolutional Network Model for Highly-accurate Node Classification on Weakly Supervised Graphs",
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{\textquoteright}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.",
keywords = "Auxiliary Learning Task, Graph Convolutional Network, Weakly Supervised Graph",
author = "Zengmei Zhuo and Xin Luo and Zhou, {Meng Chu}",
note = "Publisher Copyright: {\textcopyright}2021 IEEE; 2021 IEEE International Conference on Smart Data Services, SMDS 2021 ; Conference date: 05-09-2021 Through 11-09-2021",
year = "2021",
doi = "10.1109/SMDS53860.2021.00033",
language = "English (US)",
series = "Proceedings - 2021 IEEE International Conference on Smart Data Services, SMDS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "192--197",
editor = "Nimanthi Atukorala and Chang, {Carl K.} and Ernesto Damiani and {Fu Lizhi}, Min and George Spanoudakis and Mudhakar Srivatsa and Zhongjie Wang and Jia Zhang",
booktitle = "Proceedings - 2021 IEEE International Conference on Smart Data Services, SMDS 2021",
address = "United States",
}