Zero-shot Domain Adaptation of Heterogeneous Graphs via Knowledge Transfer Networks

How can we make predictions for nodes in a heterogeneous graph when an entire type of node (e.g. user) has no labels (perhaps due to privacy issues) at all? Although heterogeneous graph neural networks (HGNNs) have shown superior performance as powerful representation learning techniques, there is no direct way to learn using labels rooted at different node types. Domain adaptation (DA) targets this setting, however, existing DA can not be applied directly to HGNNs. In heterogeneous graphs, the source and target domains have different modalities, thus HGNNs provide different feature extractors to them, while most of DA assumes source and target domains share a common feature extractor. In this work, we address the issue of zero-shot domain adaptation in HGNNs. We first theoretically induce a relationship between source and target domain features extracted from HGNNs, then propose a novel domain adaptation method, Knowledge Transfer Networks for HGNNs (HGNN-KTN). HGNN-KTN learns the relationship between source and target features, then maps the target distributions into the source domain. HGNN-KTN outperforms stateof-the-art baselines, showing up to 73.3% higher in MRR on 18 different domain adaptation tasks running on real-world benchmark graphs.