2021
DOI: 10.48550/arxiv.2106.08848
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification

Abstract: Data augmentation aims to generate new and synthetic features from the original data, which can identify a better representation of data and improve the performance and generalizability of downstream tasks. However, data augmentation for graph-based models remains a challenging problem, as graph data is more complex than traditional data, which consists of two features with different properties: graph topology and node attributes. In this paper, we study the problem of graph data augmentation for Graph Convolu… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 26 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?