A Survey on Deep Graph Generation: Methods and Applications. (arXiv:2203.06714v2 [cs.LG] UPDATED)

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
graph generation from a variety of emerging methods to its wide application
areas. Specifically, we first formulate the problem of deep graph generation
and …

applications arxiv generation graph survey