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

Tao Wen, Elynn Chen, Yuzhou Chen, Qi Lei
Conference on Parsimony and Learning, PMLR 280:599-614, 2025.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v280-wen25a, title = {Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation}, author = {Wen, Tao and Chen, Elynn and Chen, Yuzhou and Lei, Qi}, booktitle = {Conference on Parsimony and Learning}, pages = {599--614}, year = {2025}, editor = {Chen, Beidi and Liu, Shijia and Pilanci, Mert and Su, Weijie and Sulam, Jeremias and Wang, Yuxiang and Zhu, Zhihui}, volume = {280}, series = {Proceedings of Machine Learning Research}, month = {24--27 Mar}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v280/main/assets/wen25a/wen25a.pdf}, url = {https://proceedings.mlr.press/v280/wen25a.html}, 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.} }
Endnote
%0 Conference Paper %T Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation %A Tao Wen %A Elynn Chen %A Yuzhou Chen %A Qi Lei %B Conference on Parsimony and Learning %C Proceedings of Machine Learning Research %D 2025 %E Beidi Chen %E Shijia Liu %E Mert Pilanci %E Weijie Su %E Jeremias Sulam %E Yuxiang Wang %E Zhihui Zhu %F pmlr-v280-wen25a %I PMLR %P 599--614 %U https://proceedings.mlr.press/v280/wen25a.html %V 280 %X 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.
APA
Wen, T., Chen, E., Chen, Y. & Lei, Q.. (2025). Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation. Conference on Parsimony and Learning, in Proceedings of Machine Learning Research 280:599-614 Available from https://proceedings.mlr.press/v280/wen25a.html.

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