The (variational) graph auto-encoder is extensively employed for learning
representations of graph-structured data. However, the formation of real-world
graphs is a complex and heterogeneous process influenced by latent factors.
Existing encoders are fundamentally holistic, neglecting the entanglement of
latent factors. This not only makes graph analysis tasks less effective but
also makes it harder to understand and explain the representations. Learning
disentangled graph representations with (variational) graph auto-encoder poses
significant challenges, and remains largely unexplored in the existing
literature. In this article, we introduce the Disentangled Graph Auto-Encoder
(DGA) and Disentangled Variational Graph Auto-Encoder (DVGA), approaches that
leverage generative models to learn disentangled representations. Specifically,
we first design a disentangled graph convolutional network with multi-channel
message-passing layers, as the encoder aggregating information related to each
disentangled latent factor. Subsequently, a component-wise flow is applied to
each channel to enhance the expressive capabilities of disentangled variational
graph auto-encoder. Additionally, we design a factor-wise decoder, considering
the characteristics of disentangled representations. In order to further
enhance the independence among representations, we introduce independence
constraints on mapping channels for different latent factors. Empirical
experiments on both synthetic and real-world datasets show the superiority of
our proposed method compared to several state-of-the-art baselines.

我们介绍了 “解缠图自编码器” 和 “解缠变分图自编码器”（DGA 和 DVGA）的方法，其利用生成模型学习解缠表示，通过设计多通道信息传递层的解缠图卷积网络作为编码器，以及引入在不同潜在因素上的独立性约束，我们的方法在合成和真实数据集上实验表明其优越性。