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Structure enhanced prototypical alignment for unsupervised cross-domain node classification
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DOI：10.1016/j.neunet.2024.106396.png)
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Abstract
En 中文
Graph Neural Networks (GNNs) have demonstrated remarkable success in graph node classification task. However, their performance heavily relies on the availability of high -quality labeled data, which can be time-consuming and labor-intensive to acquire for graph -structured data. Therefore, the task of transferring knowledge from a label -rich graph (source domain) to a completely unlabeled graph (target domain) becomes crucial. In this paper, we propose a novel unsupervised graph domain adaptation framework called Structure Enhanced Prototypical Alignment (SEPA), which aims to learn domain -invariant representations on nonIID (non -independent and identically distributed) data. Specifically, SEPA captures class -wise semantics by constructing a prototype -based graph and introduces an explicit domain discrepancy metric to align the source and target domains. The proposed SEPA framework is optimized in an end -to -end manner, which could be incorporated into various GNN architectures. Experimental results on several real -world datasets demonstrate that our proposed framework outperforms recent state-of-the-art baselines with different gains.
Keywords:
Graph domain adaptation
Graph neural networks
Node classification
Graph representation learning
Transfer learning
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