  Domain adaptation using graph networks is to learn label-discriminative and
network-invariant node embeddings by sharing graph parameters. Most existing
works focus on domain adaptation of homogeneous networks, and just a few works
begin to study heterogeneous cases that only consider the shared node types but
ignore the private node types in individual networks. However, for a given
source and target heterogeneous networks, they generally contain shared and
private node types, where private types bring an extra challenge for graph
domain adaptation. In this paper, we investigate Heterogeneous Information
Networks (HINs) with partial shared node types and propose a novel domain
adaptive heterogeneous graph transformer (DA-HGT) to handle the domain shift
between them. DA-HGT can not only align the distributions of identical-type
nodes and edges in two HINs but also make full use of different-type nodes and
edges to improve the performance of knowledge transfer. Extensive experiments
on several datasets demonstrate that DA-HGT can outperform state-of-the-art
methods in various domain adaptation tasks across heterogeneous networks.
