GraphTune: A Learning-based Graph Generative Model with Tunable Structural Features
Abstract
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.
Cite
@article{arxiv.2201.11494,
title = {GraphTune: A Learning-based Graph Generative Model with Tunable Structural Features},
author = {Kohei Watabe and Shohei Nakazawa and Yoshiki Sato and Sho Tsugawa and Kenji Nakagawa},
journal= {arXiv preprint arXiv:2201.11494},
year = {2023}
}
Comments
The paper was published in IEEE Transactions on Network Science and Engineering (2023). An earlier and short version of this paper was presented at the 41st IEEE International Conference on Distributed Computing Systems (ICDCS 2021) Poster Track