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.

翻译：图形生成模型已经被广泛研究几十年，并且它们具有很多应用。最近，越来越多的研究者开始关注基于学习的图形生成，以复现真实世界中的图形。尽管已经提出了几种利用现代机器学习技术的生成模型，但是在场中尚未很好地探索一般图形的条件生成。本文提出了一种生成模型，该生成模型允许我们将全局结构特征的值调整为条件。我们的模型称为 GraphTune，它使用长短期记忆（LSTM）和条件变分自动编码器（CVAE）来调整生成图的任何结构特征的值。我们在真实图形数据集上对 GraphTune 和传统模型进行了比较评估。评估结果表明，GraphTune 使调整全局结构特征的值比传统模型更容易明确。