An Accurate Graph Generative Model with Tunable Features
This work addresses the need for more precise graph generation with tunable features, which is incremental as it builds on an existing model to improve accuracy for applications like network simulations.
The paper tackles the problem of insufficient tuning accuracy in graph generative models by enhancing GraphTune with a feedback mechanism and independent training, achieving more accurate feature tuning compared to conventional models.
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.