Structurally Enhanced Prototype Alignment for Unsupervised Cross-Domain Node Classification

Introduction

With the advancement of modern information technology, Graph Neural Networks (GNNs) have demonstrated significant success in handling complex network node classification tasks. However, one key challenge is the need for a large amount of high-quality labeled data, which is costly and time-consuming to obtain for graph-structured data. Therefore, how to transfer knowledge from a richly labeled graph (source domain) to a completely unlabeled graph (target domain) has become an urgent issue to address.

Research Background and Objectives

The research team, hailing from the College of Computer Science at Zhejiang University, the Zhejiang Provincial Key Laboratory of Service Robot, and the School of Computing at the National University of Singapore, proposed a novel unsupervised graph domain adaptation framework named Structurally Enhanced Prototype Alignment (SEPA). The aim is to achieve alignment between the source and target domains by constructing prototype-based graphs and introducing explicit domain discrepancy measures. The paper was published in the journal “Neural Networks,” and a series of experiments have demonstrated its superior performance on multiple real-world datasets.

Method Overview and Workflow

Research Objects and Workflow

In this study, both the source and target graphs contain several nodes and edges, with differences in the distribution of node attributes and labels. The specific workflow includes the following steps:

1. Basic Prototype Estimation: A supervised classifier from the source domain is first used to make initial predictions on the target graph’s nodes. Based on these predictions, initial prototypes for each category in the target graph are averaged.
2. Transition Matrix Estimation: Construct a transition matrix to represent the relationship between true labels and pseudo-labels, mitigating label uncertainty caused by domain transfer.
3. Constructing Prototype-based Graph: Assign each target node a soft prototype based on the transition matrix, then construct a prototype-based graph using these soft prototypes.
4. Prototype-based Graph Propagation: Perform feature propagation on the constructed prototype graph to update the soft prototypes.
5. Prototype Alignment: Implement inter-class alignment through explicit alignment loss functions to reduce discrepancies between the source and target domains.

Experimental Methods and Technical Details

To evaluate SEPA’s performance, the authors conducted experiments on multiple real-world datasets, including citation networks (such as ACM, Microsoft Academic Graph, DBLP) and social networks (such as the Twitch game social network). In these datasets, nodes represent papers or users, and edges represent citation or social relationships. Extensive experiments validated the effectiveness of the SEPA framework.

Data Analysis and Algorithm Implementation

In terms of data analysis and algorithm implementation, the SEPA framework optimizes through a self-supervised approach, avoiding issues caused by traditional pseudo-labeling methods. Its core lies in iteratively updating prototypes and node features to ensure that the structural characteristics of the target graph better reflect its inherent semantics, thereby achieving more accurate inter-class alignment.

Main Research Results

Experimental Results

In multiple experimental scenarios, the SEPA framework outperforms the latest baseline models in terms of micro-F1 and macro-F1 metrics. For instance, in the cross-domain node classification task from ACM to Microsoft Academic Graph, the SEPA framework achieved 74.85% and 73.83% accuracy in macro-F1 and micro-F1, respectively, significantly surpassing other methods.

Effectiveness of the Method

Detailed analysis of different components and loss functions reveals that each part contributes to the model’s final performance. The model performs poorly when only considering source domain information; adding domain alignment loss significantly improves performance, and further integrating target domain predictions leads to the optimal performance. The implementation form of prototype alignment also validates the effectiveness of the self-supervised alignment approach.

Parameter Sensitivity Analysis

Further parameter sensitivity analysis shows that SEPA has stable robustness in selecting major hyperparameters, with minimal impact on results within a reasonable range. This indicates that the SEPA framework possesses strong stability and applicability during the optimization process.

Visualization Verification

Through visualization analysis of target domain embeddings, SEPA-generated embeddings exhibit clearer inter-class separability, validating its superiority in learning discriminative embeddings. In t-SNE projection plots, nodes of different categories are better separated, further demonstrating SEPA’s effectiveness in reducing domain discrepancies.

Research Conclusions

Conclusions and Significance

The SEPA framework proposed in this paper performs excellently in unsupervised cross-domain node classification tasks, effectively capturing semantic relationships between categories and achieving efficient alignment between the source and target domains through structurally enhanced prototype alignment. SEPA provides a new solution for domain adaptation. It has demonstrated its superiority on multiple real-world datasets and showcased the flexibility and robustness of the framework, offering an important reference for future related research.

Future Directions

This study provides new perspectives on unsupervised cross-domain node classification tasks, combining structural features of complex networks with semantic alignment methods. It holds significant scientific value and application prospects. Future work could consider expanding the approach to more types of graph data and more complex graph structures, enhancing its applicability in different real-world applications. Additionally, further optimization of algorithm performance to improve training efficiency and stability will also be an important direction for future research.

Highlights and Innovations

The SEPA framework proposed in this paper has the following innovations: 1. Introducing a structurally enhanced prototype alignment method that captures inter-class semantic relationships in unsupervised cross-domain node classification tasks for the first time. 2. Constructing a prototype-based graph effectively integrates the structural information of the target domain into the alignment process, improving the model’s applicability and accuracy. 3. Experimental results show that SEPA outperforms existing baseline models on multiple real-world datasets, demonstrating the framework’s robustness and generalizability.

The SEPA framework proposed in this paper holds significant theoretical and practical value in the field of unsupervised cross-domain node classification, offering new ideas and methods for addressing domain adaptation issues in graph-structured data. Future research can further expand and optimize the approach to tackle more complex and diverse application scenarios.