  A graph is a very common and powerful data structure used for modeling
communication and social networks. Models that generate graphs with arbitrary
features are important basic technologies in repeated simulations of networks
and prediction of topology changes. Although existing generative models for
graphs are useful for providing graphs similar to real-world graphs, graph
generation models with tunable features have been less explored in the field.
Previously, we have proposed GraphTune, a generative model for graphs that
continuously tune specific graph features of generated graphs while maintaining
most of the features of a given graph dataset. However, the tuning accuracy of
graph features in GraphTune has not been sufficient for practical applications.
In this paper, we propose a method to improve the accuracy of GraphTune by
adding a new mechanism to feed back errors of graph features of generated
graphs and by training them alternately and independently. Experiments on a
real-world graph dataset showed that the features in the generated graphs are
accurately tuned compared with conventional models.
