Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation

• URL: http://arxiv.org/abs/2502.08505v2
• Date: Sat, 15 Feb 2025 16:00:16 GMT
• Title: Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation
• Authors: Tao Wen, Elynn Chen, Yuzhou Chen, Qi Lei,
• Abstract summary: Graph Neural Networks (GNNs) have recently become the predominant tools for studying graph data. Despite state-of-the-art performance on graph classification tasks, GNNs are overwhelmingly trained in a single domain under supervision. We propose the Label-Propagation Graph Neural Network (LP-TGNN) framework to bridge the gap between graph data and traditional domain adaptation methods.
• Score: 23.79865440689265
• License:
• Abstract: Graph Neural Networks (GNNs) have recently become the predominant tools for studying graph data. Despite state-of-the-art performance on graph classification tasks, GNNs are overwhelmingly trained in a single domain under supervision, thus necessitating a prohibitively high demand for labels and resulting in poorly transferable representations. To address this challenge, we propose the Label-Propagation Tensor Graph Neural Network (LP-TGNN) framework to bridge the gap between graph data and traditional domain adaptation methods. It extracts graph topological information holistically with a tensor architecture and then reduces domain discrepancy through label propagation. It is readily compatible with general GNNs and domain adaptation techniques with minimal adjustment through pseudo-labeling. Experiments on various real-world benchmarks show that our LP-TGNN outperforms baselines by a notable margin. We also validate and analyze each component of the proposed framework in the ablation study.

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