When adapting graph neural networks across domains, you need to explicitly handle changes in graph topology and structure—not just features—using techniques like topological moment matching and spectral calibration.
This paper tackles graph domain adaptation by addressing structural differences between source and target graphs, not just feature differences. It proposes DSBD, which learns a flexible structural basis that can be adapted across domains while preserving important graph properties like geometry and spectral characteristics, then trains a fresh neural network on this adapted structure.