Structure enhanced prototypical alignment for unsupervised cross-domain node classification.

Area of Science:

• Computer Science
• Machine Learning
• Artificial Intelligence

Background:

• Graph Neural Networks (GNNs) excel at node classification but require extensive labeled data.
• Acquiring labeled data for graph-structured datasets is often costly and time-consuming.
• Domain adaptation is essential for applying models trained on one graph to another unlabeled graph.

Purpose of the Study:

• To develop a novel unsupervised graph domain adaptation framework.
• To enable knowledge transfer from label-rich source graphs to unlabeled target graphs.
• To learn domain-invariant representations for non-independent and identically distributed (non-IID) graph data.

Main Methods:

• Propose Structure Enhanced Prototypical Alignment (SEPA), a framework for unsupervised graph domain adaptation.
• Construct a prototype-based graph to capture class-wise semantics.
• Introduce an explicit domain discrepancy metric for aligning source and target domains.
• Optimize the SEPA framework in an end-to-end manner, compatible with various GNN architectures.

Main Results:

• SEPA effectively learns domain-invariant representations.
• The framework demonstrates superior performance compared to state-of-the-art baselines on real-world datasets.
• Achieved significant performance gains in graph node classification tasks through domain adaptation.

Conclusions:

• SEPA provides an effective solution for unsupervised graph domain adaptation.
• The proposed method addresses the challenge of limited labeled data in graph-structured domains.
• SEPA enhances the generalizability of GNNs across different graph domains.