Authors:
Jinhui Pang、Zixuan Wang、Jiliang Tang、Mingyan Xiao、Nan Yin
Paper:
https://arxiv.org/abs/2408.09189
Introduction
Graph neural networks (GNNs) have demonstrated remarkable performance in various graph-related tasks, such as node classification. However, most GNNs are designed for supervised learning within a single domain, requiring extensive labeled data. This limitation poses challenges when transferring models to new domains with scarce labels. Addressing this issue, the paper introduces Spectral Augmentation for Graph Domain Adaptation (SA-GDA), a novel approach for unsupervised domain adaptation in graph node classification. The key idea is to leverage spectral domain characteristics to align category features across different domains, thereby improving classification performance in the target domain.
Related Work
Graph Neural Networks
Graph neural networks (GNNs) are designed to learn node embeddings by mapping graph nodes and their relationships to a low-dimensional latent space. Various GNN models, such as GCN and GAT, have been developed to handle tasks like node classification and relationship prediction. However, these models often struggle with domain adaptation due to representation space drift and embedding distribution discrepancies when transferring between different graphs.
Unsupervised Domain Adaptation
Unsupervised domain adaptation aims to minimize the discrepancy between source and target domains, enabling knowledge transfer from a well-labeled source domain to an unlabeled target domain. Methods like DANN and CDNE have been proposed to address this challenge, often using adversarial training to reduce inter-domain discrepancies. However, these methods typically align the entire feature space without considering specific class alignment, leading to potential feature confusion.
Research Methodology
Problem Definition
The problem of graph node classification involves predicting the category of each node in a graph. Given a fully labeled source graph and an unlabeled target graph with distinct data distributions, the goal is to train a classifier that can accurately classify nodes in the target domain.
Overview of SA-GDA
SA-GDA consists of three main components:
1. Spectral Augmentation Module: Combines spectral features from both source and target domains to align category features.
2. Dual Graph Learning Module: Extracts local and global consistency information using a dual graph neural network.
3. Domain Adversarial Module: Differentiates source and target domains to facilitate knowledge transfer.
Experimental Design
Spectral Augmentation for Category Alignment
Traditional domain adaptation methods often rely on pseudo-labeling, which can introduce noise and biases. Instead, SA-GDA leverages the inherent similarity of spectral features for nodes of the same category across different domains. By applying low-pass and high-pass filters, the model separates low and high-frequency signals and combines them to enhance node representation learning.
Attention-based Dual Graph Learning
To capture both local and global information, SA-GDA employs a dual graph neural network. The local GNN uses the adjacency matrix to extract local consistency, while the global GNN uses a random walk method to capture global consistency. An attention mechanism is then used to fuse the local and global representations.
Domain Adversarial Training
The domain adversarial module aims to maximize domain classification error while minimizing source domain classification error. This is achieved using a gradient reversal layer, which helps to reduce domain discrepancies and improve target domain classification accuracy.
Results and Analysis
Performance Comparison
SA-GDA was evaluated on six cross-domain node classification tasks using three citation networks: DBLPv7, ACMv9, and Citationv1. The results show that SA-GDA outperforms state-of-the-art methods, demonstrating its effectiveness in reducing domain discrepancies and improving classification accuracy.
Ablation Study
An ablation study was conducted to evaluate the impact of each component of SA-GDA. The results indicate that both low and high-frequency signals, global consistency extraction, domain adversarial training, and target classification loss contribute significantly to the model’s performance.
Sensitivity Analysis
The sensitivity analysis examined the impact of hyperparameters on the model’s performance. The results suggest that appropriate values for spectral augmentation ratio and balance ratios for target classification and domain adversarial loss are crucial for achieving optimal performance.
Visualization
To provide an intuitive understanding of SA-GDA’s effectiveness, the node representations learned in the target domain were visualized using t-SNE. The visualization shows that SA-GDA produces clearer clustering boundaries and more meaningful node representations compared to other methods.
Overall Conclusion
SA-GDA addresses the challenge of unsupervised domain adaptation for graph node classification by leveraging spectral domain characteristics for category alignment. The dual graph learning module captures both local and global information, while the domain adversarial module reduces domain discrepancies. Extensive experiments demonstrate that SA-GDA outperforms existing methods, making it a promising approach for cross-domain node classification tasks. Future work will focus on improving the efficiency of spectral domain alignment and exploring spatial domain features to further enhance category feature alignment.
