  Recent years have witnessed tremendous interest in deep learning on
graph-structured data. Due to the high cost of collecting labeled
graph-structured data, domain adaptation is important to supervised graph
learning tasks with limited samples. However, current graph domain adaptation
methods are generally adopted from traditional domain adaptation tasks, and the
properties of graph-structured data are not well utilized. For example, the
observed social networks on different platforms are controlled not only by the
different crowd or communities but also by the domain-specific policies and the
background noise. Based on these properties in graph-structured data, we first
assume that the graph-structured data generation process is controlled by three
independent types of latent variables, i.e., the semantic latent variables, the
domain latent variables, and the random latent variables. Based on this
assumption, we propose a disentanglement-based unsupervised domain adaptation
method for the graph-structured data, which applies variational graph
auto-encoders to recover these latent variables and disentangles them via three
supervised learning modules. Extensive experimental results on two real-world
datasets in the graph classification task reveal that our method not only
significantly outperforms the traditional domain adaptation methods and the
disentangled-based domain adaptation methods but also outperforms the
state-of-the-art graph domain adaptation algorithms.
