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Graph neural networks (GNN) are a powerful tool for combining imaging andnon-imaging medical information for node classification tasks. Cross-networknode classification extends GNN techniques to account for domain drift,allowing for node classification on an unlabeled target network. In this paperwe present OTGCN, a powerful, novel approach to cross-network nodeclassification. This approach leans on concepts from graph convolutionalnetworks to harness insights from graph data structures while simultaneouslyapplying strategies rooted in optimal transport to correct for the domain driftthat can occur between samples from different data collection sites. Thisblended approach provides a practical solution for scenarios with many distinctforms of data collected across different locations and equipment. Wedemonstrate the effectiveness of this approach at classifying Autism SpectrumDisorder subjects using a blend of imaging and non-imaging data.

Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups