Zero-shot Transfer Learning on Heterogeneous Graphs via Knowledge Transfer Networks. (arXiv:2203.02018v3 [cs.LG] UPDATED)

Data continuously emitted from industrial ecosystems such as social or
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 …

arxiv graphs knowledge learning lg networks transfer transfer learning