Unsupervised Discovery of Steerable Factors When Graph Deep Generative
  Models Are Entangled
Shengchao Liu Chengpeng Wang Jiarui Lu Weili Nie Hanchen Wang Zhuoxinran Li Bolei Zhou Jian Tang
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.