  Domain adaptation using graph-structured networks learns label-discriminative
and network-invariant node embeddings by sharing graph parameters. Most
existing works focus on domain adaptation of homogeneous networks. The few
works that study heterogeneous cases only consider shared node types but ignore
private node types in individual networks. However, for 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 both
shared and private node types and propose a Generalized Domain Adaptive model
across HINs (GDA-HIN) to handle the domain shift between them. GDA-HIN can not
only align the distribution 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 GDA-HIN can outperform state-of-the-art methods in various domain
adaptation tasks across heterogeneous networks.
