Deep generative models (DGMs) have been widely developed for graph data.
However, much less investigation has been carried out on understanding the
latent space of such pretrained graph DGMs. These understandings possess the
potential to provide constructive guidelines for crucial tasks, such as graph
controllable generation. Thus in this work, we are interested in studying this
problem and propose GraphCG, a method for the unsupervised discovery of
steerable factors in the latent space of pretrained graph DGMs. We first
examine the representation space of three pretrained graph DGMs with six
disentanglement metrics, and we observe that the pretrained representation
space is entangled. Motivated by this observation, GraphCG learns the steerable
factors via maximizing the mutual information between semantic-rich directions,
where the controlled graph moving along the same direction will share the same
steerable factors. We quantitatively verify that GraphCG outperforms four
competitive baselines on two graph DGMs pretrained on two molecule datasets.
Additionally, we qualitatively illustrate seven steerable factors learned by
GraphCG on five pretrained DGMs over five graph datasets, including two for
molecules and three for point clouds.

本研究提出了一种用于在预训练图形生成模型的潜在空间中无监督发现可控因素的方法，并通过最大化语义丰富的方向之间的互信息来学习可控因素。验证结果表明，该方法在两个预训练图形生成模型上优于其他四种竞争算法，并在两种分子数据集和三个点云数据集上展示了七个可控因素的质量。