  Data continuously emitted from industrial ecosystems such as social or
e-commerce platforms are commonly represented as heterogeneous graphs (HG)
composed of multiple node/edge types. State-of-the-art graph learning methods
for HGs known as heterogeneous graph neural networks (HGNNs) are applied to
learn deep context-informed node representations. However, many HG datasets
from industrial applications suffer from label imbalance between node types. As
there is no direct way to learn using labels rooted at different node types,
HGNNs have been applied to only a few node types with abundant labels. We
propose a zero-shot transfer learning module for HGNNs called a Knowledge
Transfer Network (KTN) that transfers knowledge from label-abundant node types
to zero-labeled node types through rich relational information given in the HG.
KTN is derived from the theoretical relationship, which we introduce in this
work, between distinct feature extractors for each node type given in an HGNN
model. KTN improves performance of 6 different types of HGNN models by up to
960% for inference on zero-labeled node types and outperforms state-of-the-art
transfer learning baselines by up to 73% across 18 different transfer learning
tasks on HGs.

Machine Learning