Feb 16, 2022 · In this survey, we formally formulate the problem of graph data augmentation and further review the representative techniques and their ...
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In this survey, we formally formulate the problem of graph data augmentation and further review the representative techniques and their applications in ...
Graph neural networks, a powerful deep learning tool to model graph-structured data, have demonstrated remark- able performance on numerous graph learning tasks ...
Nov 21, 2022 · This survey is structured as follows. Section 2 first gives background on graph neural networks. (GNNs) and deep graph learning. Then in Section ...
This survey provides a comprehensive review and summary of existing graph data augmentation (GDAug) techniques and outlines the applications of GDAug at both ...
Nov 29, 2022 · The unique capability of graphs enables capturing the structural relations among data, and thus allows to harvest more insights compared to ...
To address the data noise and data scarcity issues in deep graph learning, the research on graph data augmentation has intensified lately. However, conventional ...
This repository contains a list of papers on the Graph Data Augmentation, we categorize them based on their learning objectives and tasks.
To counter the data noise and data scarcity issues in deep graph learning (DGL), increasing graph data augmentation research has been conducted lately. However, ...
Feb 16, 2022 · In this survey, we formally formulate the problem of graph data augmentation and further review the representative techniques in this field.