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：图生成模型已被积极研究了数十年，并拥有广泛的应用。近年来，能够重现真实世界图结构的学习型图生成方法正吸引着众多研究者的关注。尽管已有多种利用现代机器学习技术的生成模型被提出，但面向通用图的条件生成研究领域仍相对薄弱。本文提出了一种以全局级结构特征值为条件进行调控的生成模型。我们的模型名为GraphTune，通过长短时记忆网络和条件变分自编码器实现对所生成图任意结构特征值的调控。我们在真实图数据集上对GraphTune与现有模型进行了对比评估。评估结果表明，相比现有模型，GraphTune能够更清晰地调控全局级结构特征的值。