Effect of Retraining Graph Generative Models with Generated Graphs
T. Inada, Sho Tsugawa, Akiko Manada, Kohei Watabe · 2024
In recent years, there has been a growing demand for techniques to artificially generate graphs. Various proposals have been made for graph generation models using machine learning. Among these models, GraphTune is a model that allows to specify the features of the generated graphs. GraphTune has not achieved sufficient accuracy when specified values are in ranges where there are few samples in the training dataset. Therefore, in this paper, we propose a method to improve the accuracy of GraphTune by retraining it using graphs generated by the model itself. Through experiments using real-world graphs, we demonstrate that the higher accuracy can be achieved compared to the conventional method.