  Generative models for graphs have been actively studied for decades, and they
have a wide range of applications. Recently, learning-based graph generation
that reproduces real-world graphs has been attracting the attention of many
researchers. Although several generative models that utilize modern machine
learning technologies have been proposed, conditional generation of general
graphs has been less explored in the field. In this paper, we propose a
generative model that allows us to tune the value of a global-level structural
feature as a condition. Our model, called GraphTune, makes it possible to tune
the value of any structural feature of generated graphs using Long Short Term
Memory (LSTM) and a Conditional Variational AutoEncoder (CVAE). We performed
comparative evaluations of GraphTune and conventional models on a real graph
dataset. The evaluations show that GraphTune makes it possible to more clearly
tune the value of a global-level structural feature better than conventional
models.
