  Graphs are important data representations for describing objects and their
relationships, which appear in a wide diversity of real-world scenarios. As one
of a critical problem in this area, graph generation considers learning the
distributions of given graphs and generating more novel graphs. Owing to their
wide range of applications, generative models for graphs, which have a rich
history, however, are traditionally hand-crafted and only capable of modeling a
few statistical properties of graphs. Recent advances in deep generative models
for graph generation is an important step towards improving the fidelity of
generated graphs and paves the way for new kinds of applications. This article
provides an extensive overview of the literature in the field of deep
generative models for graph generation. Firstly, the formal definition of deep
generative models for the graph generation and the preliminary knowledge are
provided. Secondly, taxonomies of deep generative models for both unconditional
and conditional graph generation are proposed respectively; the existing works
of each are compared and analyzed. After that, an overview of the evaluation
metrics in this specific domain is provided. Finally, the applications that
deep graph generation enables are summarized and five promising future research
directions are highlighted.
