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

Abstract

Graph-structured data is prevalent in numerous fields, but the scarcity of labeled instances often limits the effective application of deep learning techniques. Traditional unsupervised domain adaptation (UDA) strategies for graphs typically rely on adversarial learning and pseudo-labeling. However, these methods often fail to leverage the discriminative features of graphs, resulting in class mismatches and unreliable label quality. To overcome these challenges, we developed the Denoising and Nuclear-Norm Wasserstein Adaptation Network (DNAN). DNAN utilizes the Nuclear-Norm Wasserstein Discrepancy (NWD), which simultaneously achieves domain alignment and class distinction. The NWD is integrated with a denoising mechanism using a variational graph autoencoder, with a theoretical analysis provided for the denoising process. This denoising mechanism aims to address domain shifts in structural patterns between the source and target domains. Our comprehensive experiments demonstrate that DNAN outperforms state-of-the-art methods on standard UDA benchmarks for graph classification, highlighting its effectiveness and robustness.