Graph Neural Networks (GNNs) require a large number of labeled graph samples
to obtain good performance on the graph classification task. The performance of
GNNs degrades significantly as the number of labeled graph samples decreases.
To reduce the annotation cost, it is therefore important to develop graph
augmentation methods that can generate new graph instances to increase the size
and diversity of the limited set of available labeled graph samples. In this
work, we propose a novel mixup-based graph augmentation method, Graph Dual
Mixup (GDM), that leverages both functional and structural information of the
graph instances to generate new labeled graph samples. GDM employs a graph
structural auto-encoder to learn structural embeddings of the graph samples,
and then applies mixup to the structural information of the graphs in the
learned structural embedding space and generates new graph structures from the
mixup structural embeddings. As for the functional information, GDM applies
mixup directly to the input node features of the graph samples to generate
functional node feature information for new mixup graph instances. Jointly, the
generated input node features and graph structures yield new graph samples
which can supplement the set of original labeled graphs. Furthermore, we
propose two novel Balanced Graph Sampling methods to enhance the balanced
difficulty and diversity for the generated graph samples. Experimental results
on the benchmark datasets demonstrate that our proposed method substantially
outperforms the state-of-the-art graph augmentation methods when the labeled
graphs are scarce.

本文提出了一种新颖的基于混合的图形增强方法，称为图形双重混合（GDM），利用图形实例的功能和结构信息生成新的标记图形样本，以增加有限可用标记图形样本的大小和多样性，并通过两种新颖的平衡图形采样方法增强生成图形样本的平衡难度和多样性。在基准数据集上的实验证明，当标记图形稀缺时，我们提出的方法明显优于最先进的图形增强方法。

GDM：有限监督下的图分类的双重混合技术

GDM: Dual Mixup for Graph Classification with Limited Supervision

As a new approach to train generative models, \emph{generative adversarial
networks} (GANs) have achieved considerable success in image generation. This
framework has also recently been applied to data with graph structures. We
propose labeled-graph generative adversarial networks (LGGAN) to train deep
generative models for graph-structured data with node labels. We test the
approach on various types of graph datasets, such as collections of citation
networks and protein graphs. Experiment results show that our model can
generate diverse labeled graphs that match the structural characteristics of
the training data and outperforms all alternative approaches in quality and
generality. To further evaluate the quality of the generated graphs, we use
them on a downstream task of graph classification, and the results show that
LGGAN can faithfully capture the important aspects of the graph structure.

本研究提出了一种名为 LGGAN 的有标签图生成对抗网络，用于训练基于节点标签的图结构数据的深度生成模型，并在各种类型的图数据集上进行了测试，其结果表明我们的模型可以生成符合训练数据的结构特征的多样化的标记图，并优于所有另一种方法的质量和普适性。此外，经过下游任务进行的生成图的质量评价，结果表明 LGGAN 可以忠实地捕捉图结构的重要方面。

标记图生成对抗网络

Labeled Graph Generative Adversarial Networks

We prove that the optimal assignment kernel, proposed recently as an attempt
to embed labeled graphs and more generally tuples of basic data to a Hilbert
space, is in fact not always positive definite.

本文研究了一种将标记图及基本数据元组嵌入到 Hilbert 空间的最优分配核技术，并证明它并不总是正定的。