Random-Based and Deep Graph Generators: Evolution and Future Prospects

Graphs are highly flexible data structures that can model various data and relationships. By using graphs, we can abstract and represent various things in the real world. The technology of artificially generating graphs is important in various fields where graphs are applied to various fields in engineering, including communication networks, social networks, and so on. In this paper, we organize and introduce graph generation techniques from early random-based methods to the latest deep graph generators, focusing on the aspects of feature reproduction and specification. Techniques for reproducing and specifying graph features in graph generation may provide new research methods for classical graph theory and optimization problems on graphs. This paper also presents recent achievements that may lead to further exploration in these fields and discusses the future prospects of graph generation.