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Graph-structured data can be found in numerous domains, yet the scarcity oflabeled instances hinders its effective utilization of deep learning in manyscenarios. Traditional unsupervised domain adaptation (UDA) strategies forgraphs primarily hinge on adversarial learning and pseudo-labeling. Theseapproaches fail to effectively leverage graph discriminative features, leadingto class mismatching and unreliable label quality. To navigate these obstacles,we develop the Denoising and Nuclear-Norm Wasserstein Adaptation Network(DNAN). DNAN employs the Nuclear-norm Wasserstein discrepancy (NWD), which cansimultaneously achieve domain alignment and class distinguishment. DANA alsointegrates a denoising mechanism via a variational graph autoencoder thatmitigates data noise. This denoising mechanism helps capture essential featuresof both source and target domains, improving the robustness of the domainadaptation process. Our comprehensive experiments demonstrate that DNANoutperforms state-of-the-art methods on standard UDA benchmarks for graphclassification.

Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data