Data Augmentation on Graphs: A Technical Survey
- URL: http://arxiv.org/abs/2212.09970v3
- Date: Fri, 21 Jun 2024 05:50:54 GMT
- Title: Data Augmentation on Graphs: A Technical Survey
- Authors: Jiajun Zhou, Chenxuan Xie, Shengbo Gong, Zhenyu Wen, Xiangyu Zhao, Qi Xuan, Xiaoniu Yang,
- Abstract summary: graph representation learning has achieved remarkable success while suffering from low-quality data problems.
Data augmentation has also attracted increasing attention in graph domain.
This survey provides a comprehensive review and overview of graph data augmentation techniques.
- Score: 22.15025123705249
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems. As a mature technology to improve data quality in computer vision, data augmentation has also attracted increasing attention in graph domain. To advance research in this emerging direction, this survey provides a comprehensive review and summary of existing graph data augmentation (GDAug) techniques. Specifically, this survey first provides an overview of various feasible taxonomies and categorizes existing GDAug studies based on multi-scale graph elements. Subsequently, for each type of GDAug technique, this survey formalizes standardized technical definition, discuss the technical details, and provide schematic illustration. The survey also reviews domain-specific graph data augmentation techniques, including those for heterogeneous graphs, temporal graphs, spatio-temporal graphs, and hypergraphs. In addition, this survey provides a summary of available evaluation metrics and design guidelines for graph data augmentation. Lastly, it outlines the applications of GDAug at both the data and model levels, discusses open issues in the field, and looks forward to future directions. The latest advances in GDAug are summarized in GitHub.
Related papers
- Towards Data-centric Machine Learning on Directed Graphs: a Survey [23.498557237805414]
We introduce a novel taxonomy for existing studies of directed graph learning.
We re-examine these methods from the data-centric perspective, with an emphasis on understanding and improving data representation.
We identify key opportunities and challenges within the field, offering insights that can guide future research and development in directed graph learning.
arXiv Detail & Related papers (2024-11-28T06:09:12Z) - Graph Domain Adaptation: Challenges, Progress and Prospects [61.9048172631524]
We propose graph domain adaptation as an effective knowledge-transfer paradigm across graphs.
GDA introduces a bunch of task-related graphs as source graphs and adapts the knowledge learnt from source graphs to the target graphs.
We outline the research status and challenges, propose a taxonomy, introduce the details of representative works, and discuss the prospects.
arXiv Detail & Related papers (2024-02-01T02:44:32Z) - Towards Data-centric Graph Machine Learning: Review and Outlook [120.64417630324378]
We introduce a systematic framework, Data-centric Graph Machine Learning (DC-GML), that encompasses all stages of the graph data lifecycle.
A thorough taxonomy of each stage is presented to answer three critical graph-centric questions.
We pinpoint the future prospects of the DC-GML domain, providing insights to navigate its advancements and applications.
arXiv Detail & Related papers (2023-09-20T00:40:13Z) - A Comprehensive Survey on Graph Summarization with Graph Neural Networks [21.337505372979066]
In the past, most graph summarization techniques sought to capture the most important part of a graph statistically.
Today, the high dimensionality and complexity of modern graph data are making deep learning techniques more popular.
Our investigation includes a review of the current state-of-the-art approaches, including recurrent GNNs, convolutional GNNs, graph autoencoders, and graph attention networks.
arXiv Detail & Related papers (2023-02-13T05:43:24Z) - State of the Art and Potentialities of Graph-level Learning [54.68482109186052]
Graph-level learning has been applied to many tasks including comparison, regression, classification, and more.
Traditional approaches to learning a set of graphs rely on hand-crafted features, such as substructures.
Deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations.
arXiv Detail & Related papers (2023-01-14T09:15:49Z) - Graph Pooling for Graph Neural Networks: Progress, Challenges, and
Opportunities [128.55790219377315]
Graph neural networks have emerged as a leading architecture for many graph-level tasks.
graph pooling is indispensable for obtaining a holistic graph-level representation of the whole graph.
arXiv Detail & Related papers (2022-04-15T04:02:06Z) - Few-Shot Learning on Graphs: A Survey [92.47605211946149]
Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications.
semi-supervised graph representation learning models for specific tasks often suffer from label sparsity issue.
Few-shot learning on graphs (FSLG) has been proposed to tackle the performance degradation in face of limited annotated data challenge.
arXiv Detail & Related papers (2022-03-17T13:21:11Z) - Graph Data Augmentation for Graph Machine Learning: A Survey [19.372562034069084]
This paper aims to clarify the landscape of existing literature in graph data augmentation and motivate additional work in this area.
We first categorize graph data augmentation operations based on the components of graph data they modify or create.
Next, we introduce recent advances in graph data augmentation, separating by their learning objectives and methodologies.
arXiv Detail & Related papers (2022-02-17T19:14:17Z) - Data Augmentation for Deep Graph Learning: A Survey [66.04015540536027]
We first propose a taxonomy for graph data augmentation and then provide a structured review by categorizing the related work based on the augmented information modalities.
Focusing on the two challenging problems in DGL (i.e., optimal graph learning and low-resource graph learning), we also discuss and review the existing learning paradigms which are based on graph data augmentation.
arXiv Detail & Related papers (2022-02-16T18:30:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.