As a new way to train generative models, generative adversarial networks (GANs) have achieved considerable success in image generation, and this framework has also recently been applied to data with graph structures. We identify the drawbacks of existing deep frameworks for generating graphs, and 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 baselines in terms of quality, generality, and scalability. To further evaluate the quality of the generated graphs, we apply it to a downstream task for graph classification, and the results show that LGGAN can better 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