  Deep generative models have achieved great success in areas such as image,
speech, and natural language processing in the past few years. Thanks to the
advances in graph-based deep learning, and in particular graph representation
learning, deep graph generation methods have recently emerged with new
applications ranging from discovering novel molecular structures to modeling
social networks. This paper conducts a comprehensive survey on deep
learning-based graph generation approaches and classifies them into five broad
categories, namely, autoregressive, autoencoder-based, RL-based, adversarial,
and flow-based graph generators, providing the readers a detailed description
of the methods in each class. We also present publicly available source codes,
commonly used datasets, and the most widely utilized evaluation metrics.
Finally, we highlight the existing challenges and discuss future research
directions.
