  Graphs are ubiquitous in encoding relational information of real-world
objects in many domains. Graph generation, whose purpose is to generate new
graphs from a distribution similar to the observed graphs, has received
increasing attention thanks to the recent advances of deep learning models. In
this paper, we conduct a comprehensive review on the existing literature of
deep graph generation from a variety of emerging methods to its wide
application areas. Specifically, we first formulate the problem of deep graph
generation and discuss its difference with several related graph learning
tasks. Secondly, we divide the state-of-the-art methods into three categories
based on model architectures and summarize their generation strategies.
Thirdly, we introduce three key application areas of deep graph generation.
Lastly, we highlight challenges and opportunities in the future study of deep
graph generation. We hope that our survey will be useful for researchers and
practitioners who are interested in this exciting and rapidly-developing field.
