An Auxiliary Learning Task-Enhanced Graph Convolutional Network Model for Highly-accurate Node Classification on Weakly Supervised Graphs
Document Type
Conference Proceeding
Publication Date
1-1-2021
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.
Identifier
85125849925 (Scopus)
ISBN
[9781665400589]
Publication Title
Proceedings 2021 IEEE International Conference on Smart Data Services Smds 2021
External Full Text Location
https://doi.org/10.1109/SMDS53860.2021.00033
First Page
192
Last Page
197
Grant
CAAIXSJLJJ-2020-004B
Recommended Citation
Zhuo, Zengmei; Luo, Xin; and Zhou, Meng Chu, "An Auxiliary Learning Task-Enhanced Graph Convolutional Network Model for Highly-accurate Node Classification on Weakly Supervised Graphs" (2021). Faculty Publications. 4643.
https://digitalcommons.njit.edu/fac_pubs/4643
