SA-GDA: Spectral Augmentation for Graph Domain Adaptation

TL;DR

This paper introduces SA-GDA, a spectral augmentation method for graph domain adaptation that aligns category features in the spectral domain, improving transferability for graph node classification tasks.

Contribution

The paper proposes a novel spectral domain alignment approach for graph domain adaptation, with a dual GCN architecture and adversarial learning, addressing label scarcity and category confusion issues.

Findings

Effective in aligning category features across domains

Improves node classification accuracy on benchmark datasets

Theoretically proven stability of the spectral augmentation method

Abstract

Graph neural networks (GNNs) have achieved impressive impressions for graph-related tasks. However, most GNNs are primarily studied under the cases of signal domain with supervised training, which requires abundant task-specific labels and is difficult to transfer to other domains. There are few works focused on domain adaptation for graph node classification. They mainly focused on aligning the feature space of the source and target domains, without considering the feature alignment between different categories, which may lead to confusion of classification in the target domain. However, due to the scarcity of labels of the target domain, we cannot directly perform effective alignment of categories from different domains, which makes the problem more challenging. In this paper, we present the \textit{Spectral Augmentation for Graph Domain Adaptation (\method{})} for graph node classification. First, we observe that nodes with the same category in different domains exhibit similar characteristics in the spectral domain, while different classes are quite different. Following the observation, we align the category feature space of different domains in the spectral domain instead of aligning the whole features space, and we theoretical proof the stability of proposed \method{}. Then, we develop a dual graph convolutional network to jointly exploits local and global consistency for feature aggregation. Last, we utilize a domain classifier with an adversarial learning submodule to facilitate knowledge transfer between different domain graphs. Experimental results on a variety of publicly available datasets reveal the effectiveness of our \method{}.