  Graph neural networks (GNN) are a powerful tool for combining imaging and
non-imaging medical information for node classification tasks. Cross-network
node classification extends GNN techniques to account for domain drift,
allowing for node classification on an unlabeled target network. In this paper
we present OTGCN, a powerful, novel approach to cross-network node
classification. This approach leans on concepts from graph convolutional
networks to harness insights from graph data structures while simultaneously
applying strategies rooted in optimal transport to correct for the domain drift
that can occur between samples from different data collection sites. This
blended approach provides a practical solution for scenarios with many distinct
forms of data collected across different locations and equipment. We
demonstrate the effectiveness of this approach at classifying Autism Spectrum
Disorder subjects using a blend of imaging and non-imaging data.
