  With the development of graph applications, generative models for graphs have
been more crucial. Classically, stochastic models that generate graphs with a
pre-defined probability of edges and nodes have been studied. Recently, some
models that reproduce the structural features of graphs by learning from actual
graph data using machine learning have been studied. However, in these
conventional studies based on machine learning, structural features of graphs
can be learned from data, but it is not possible to tune features and generate
graphs with specific features. In this paper, we propose a generative model
that can tune specific features, while learning structural features of a graph
from data. With a dataset of graphs with various features generated by a
stochastic model, we confirm that our model can generate a graph with specific
features.
