GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation

TL;DR

GALA introduces a novel graph diffusion-based method for source-free domain adaptation on graph data, utilizing source-style graph reconstruction, curriculum learning, and graph jigsaw to improve alignment and robustness.

Contribution

The paper proposes GALA, a new approach combining graph diffusion, curriculum learning, and graph jigsaw for effective source-free graph domain adaptation.

Findings

GALA achieves superior performance on benchmark datasets.

The method effectively reconstructs source-style graphs from target data.

GALA enhances robustness and generalization in graph domain adaptation.

Abstract

Source-free domain adaptation is a crucial machine learning topic, as it contains numerous applications in the real world, particularly with respect to data privacy. Existing approaches predominantly focus on Euclidean data, such as images and videos, while the exploration of non-Euclidean graph data remains scarce. Recent graph neural network (GNN) approaches can suffer from serious performance decline due to domain shift and label scarcity in source-free adaptation scenarios. In this study, we propose a novel method named Graph Diffusion-based Alignment with Jigsaw (GALA), tailored for source-free graph domain adaptation. To achieve domain alignment, GALA employs a graph diffusion model to reconstruct source-style graphs from target data. Specifically, a score-based graph diffusion model is trained using source graphs to learn the generative source styles. Then, we introduce perturbations to target graphs via a stochastic differential equation instead of sampling from a prior, followed by the reverse process to reconstruct source-style graphs. We feed the source-style graphs into an off-the-shelf GNN and introduce class-specific thresholds with curriculum learning, which can generate accurate and unbiased pseudo-labels for target graphs. Moreover, we develop a simple yet effective graph-mixing strategy named graph jigsaw to combine confident graphs and unconfident graphs, which can enhance generalization capabilities and robustness via consistency learning. Extensive experiments on benchmark datasets validate the effectiveness of GALA.