mlsanityData to 7 Oct 2026

Data Augmentation for Graph Data: Recent Advancements

arXiv 2208.11973 ↗First posted 25 Aug 2022cs.LGcited by 1

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Abstract (arXiv)

Graph Neural Network (GNNs) based methods have recently become a popular tool to deal with graph data because of their ability to incorporate structural information. The only hurdle in the performance of GNNs is the lack of labeled data. Data Augmentation techniques for images and text data can not be used for graph data because of the complex and non-euclidean structure of graph data. This gap has forced researchers to shift their focus towards the development of data augmentation techniques for graph data. Most of the proposed Graph Data Augmentation (GDA) techniques are task-specific. In this paper, we survey the existing GDA techniques based on different graph tasks. This survey not only provides a reference to the research community of GDA but also provides the necessary information to the researchers of other domains.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 33 of its 71 references that are in the map. First posted on arXiv 25 Aug 2022.

Builds on

33 of 71 references.

References · 33

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21 more
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Citing papers per year: 2022: 1.

1 paper.

  1. 2022Data Augmentation on Graphs: A Technical Surveycited by 10