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.

翻译：作为培养基因模型的新方法, \ emph{ generational 对抗网络} (GANs) 已经在图像生成方面取得了相当大的成功。 这个框架最近还应用到带有图形结构的数据中。 我们建议使用标签的图形组合对抗网络(LGGAN) 来培训带有节点标签的图形结构数据深层次的基因化模型。 我们测试了各种类型的图形数据集(如引用网络和蛋白质图表的收集)的方法。 实验结果表明, 我们的模型可以产生与培训数据结构特征相匹配的多种标签图形, 并且超越所有质量和一般的替代方法。 为了进一步评估生成的图形的质量, 我们用它们来进行下游的图形分类工作, 结果显示, LGGAN 可以忠实地捕捉图结构的重要方面 。