  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.
