# G-Mixup: Graph Data Augmentation for Graph Classification

@article{Han2022GMixupGD, title={G-Mixup: Graph Data Augmentation for Graph Classification}, author={Xiaotian Han and Zhimeng Jiang and Ninghao Liu and Xia Hu}, journal={ArXiv}, year={2022}, volume={abs/2202.07179} }

This work develops mixup for graph data . Mixup has shown superiority in improving the generalization and robustness of neural networks by interpolating features and labels between two random samples. Traditionally, Mixup can work on regular, grid-like, and Euclidean data such as image or tabular data. However, it is challenging to directly adopt Mixup to augment graph data because different graphs typically: 1) have different numbers of nodes; 2) are not readily aligned; and 3) have unique…

## 21 Citations

### GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex Clustering

- Computer ScienceArXiv
- 2022

This work proposes to project graphs onto the latent space of continuous random graph models known as graphons, leverage convex clustering in this latent space to generate nonlinear data-driven mix up functions, and investigates the use of different mixup functions for labels and data samples.

### ifMixup: Interpolating Graph Pair to Regularize Graph Classification

- Computer Science
- 2021

IfMixup is proposed, which adds dummy nodes to make two graphs have the same input size and then simultaneously performs linear in- terpolation between the aligned node feature vectors and the aligned edge representations of the two graphs, resulting in superior predictive accuracy to popular graph augmentation and GNN methods.

### Data Augmentation for Deep Graph Learning: A Survey

- Computer ScienceArXiv
- 2022

A taxonomy for graph data augmentation is proposed and a structured review by categorizing the related work based on the augmented information modalities is provided, focusing on the two challenging problems in DGL (i.e., optimal graph learning and low-resource graph learning).

### Data Augmentation for Graph Data: Recent Advancements

- Computer ScienceArXiv
- 2022

This survey of the existing GDA techniques based on different graph tasks provides a reference to the research community of GDA but also provides the necessary information to the researchers of other domains.

### Data Augmentation on Graphs: A Survey

- Computer ScienceArXiv
- 2022

A comprehensively review and summarize the existing graph data augmentation (GDAug) techniques, which first summarize a variety of feasible taxonomies, and then classify existing GDAug studies based on fine-grained graph elements.

### Data Augmentation for Deep Graph Learning

- Computer ScienceSIGKDD Explor.
- 2022

A taxonomy for graph data augmentation techniques is proposed and a structured review by categorizing the related work based on the augmented information modalities is provided, which summarizes the applications of graph data Augmentation in two representative problems in data-centric deep graph learning.

### Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification

- Computer ScienceArXiv
- 2022

This paper proposes novel objective terms for the training of GNNs for node classiﬁcation, aiming to exploit all the available data and improve accuracy, and proposes a cross-validating gradients approach to enhance the learning from labelled data.

### Out-Of-Distribution Generalization on Graphs: A Survey

- Computer ScienceArXiv
- 2022

This paper comprehensively survey OOD generalization on graphs and presents a detailed review of recent advances and categorizes existing methods into three classes from conceptually different perspectives, i.e., data, model, and learning strategy.

### GraphTTA: Test Time Adaptation on Graph Neural Networks

- Computer ScienceArXiv
- 2022

A novel test time adaptation strategy named Graph Adversarial Pseudo Group Contrast (GAPGC), for graph neural networks TTA, to better adapt to the Out Of Distribution (OOD) test data and theoretical evidence that GAPGC can extract minimal sufﬁcient information for the main task from information theory perspective is provided.

### A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability

- Computer ScienceArXiv
- 2022

A systematical survey of MixDA in terms of its taxonomy, methodology, applications, and explainability is provided to serve as a roadmap to MixDA techniques and application reviews while providing promising directions for researchers interested in this exciting area.

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