Abstract
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 its wide range of applications, generative models
for graphs have a rich history, which, 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 as well as
preliminary knowledge is provided. Secondly, two taxonomies of deep
generative models for unconditional, and conditional graph generation
respectively are proposed; 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.