用张量框架融合图神经网络与领域自适应，提升标签传播效果
Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation
- 构建张量结构提取图拓扑信息，实现跨域标签传播
- 在多个真实数据集上显著优于基线模型，提升明显
- 适合需要少标注、跨域迁移的图学习任务
图神经网络（GNN）已成为研究图数据的主要工具。尽管在图分类任务中表现优异，但现有方法大多依赖单一领域的有监督训练，导致标签需求量大，模型泛化能力差。为此，本文提出标签传播张量图神经网络（LP-TGNN）框架，将图数据与传统领域自适应方法相连接。该框架通过张量架构整体提取图的拓扑信息，并利用标签传播减少域间差异。其可与通用GNN及领域自适应技术兼容，仅需伪标签即可实现最小调整。在多个真实世界基准测试中，LP-TGNN显著优于基线方法。消融实验验证并分析了各组件的有效性。
原文摘要 · Abstract (English)
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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