Cross-Domain Relation Adaptation

Abstract

We consider the challenge of establishing relationships between samples in distinct domains, A and B, using supervised data that captures the intrinsic relationships within each domain. In other words, we present a semi-supervised setting in which there are no labeled mixed-domain pairs of samples. Our method is derived based on a generalization bound and incorporates supervised terms for each domain, a domain confusion term on the learned features, and a consistency term for domain-specific relationships when considering mixed-domain sample pairs. Our findings showcase the efficacy of our approach in two disparate domains: (i) Predicting protein-protein interactions between viruses and hosts by modeling genetic sequences. (ii) Forecasting link connections within citation graphs using graph neural networks.