Structural Alignment for Multi-View Graph Domain Adaptation under Distribution Shift
Keywords:
Multi-View Graphs, Domain Adaptation, Distribution Shift, Structural AlignmentAbstract
The rapid proliferation of graph-structured data across various disciplines has necessitated the development of advanced machine learning models capable of transferring knowledge across disparate domains. However, real-world graph data frequently exhibit multiple views, representing diverse types of relationships among entities, and suffer from significant distribution shifts between source and target domains. Traditional unsupervised domain adaptation techniques primarily focus on aligning feature representations but often neglect the intricate topological structures and the complementary information embedded within multiple views. This paper investigates a novel paradigm for structural alignment in multi-view graph domain adaptation under severe distribution shifts. We propose a comprehensive theoretical framework that simultaneously leverages multi-view complementary information and enforces structural consistency across domains. By mitigating both feature-level and topology-level discrepancies, the proposed methodology ensures robust knowledge transfer. Extensive theoretical analysis and empirical evaluations demonstrate that aligning multi-view graph structures significantly enhances the generalization capabilities of graph neural networks on unlabeled target domains. The findings underscore the critical importance of preserving topological integrity during the adaptation process, offering a robust solution for complex network analysis scenarios ranging from bioinformatics to social network modeling.
References
1. Xiaoteng Ma, Yiqin Yang, Hao Hu, Qihan Liu, Jun Yang, Chongjie Zhang, Qianchuan Zhao, and Bin Liang. Offline reinforcement learning with value-based episodic memory. arXiv preprint arXiv:2110.09796, 2021b.
2. Qu, D., Ma, Y., & Wang, Y. (2026). STR-GNN: stability-regularized graph neural networks for suppressing spurious temporal variations in dynamic community detection. Scientific Reports.
3. Hamilton, W., Ying, Z., & Leskovec, J. (2017). Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems.
4. Ma, Y., & Qu, D. (2026). Machine Learning Based Markov Decision Framework for Optimizing Circular Economy Systems.Engineering Proceedings,120(1), 44.
5. Lee, Y., Gao, P., Xu, Y., Wang, Z., Li, S., & Chen, J. (2025). MEGA-GO: functions prediction of diverse protein sequence length using Multi-scalE Graph Adaptive neural network. Bioinformatics, 41 (2), btaf032.
6. Li, K., Shuang, K., & Guo, J. (2026). Bring order to the jumbled input: Layered attention for Large Language Model based Event Argument Extraction. Engineering Applications of Artificial Intelligence, 165, 113399.
7. Liang, R., Xu, S., Xie, C., Chen, J., Ren, F., Yang, S., & Yabe, T. (2026). Abstain Mask Retain Core: Time Series Prediction by Adaptive Masking Loss with Representation Consistency. Advances in Neural Information Processing Systems, 38, 99445-99471.
8. Fang, R., Chen, C., Wang, Q., Ling, C., Wang, B., & Zhang, H. (2026). Transfer learning with graph neural networks to predict polymer solubility parameters. npj Computational Materials.
9. Li, X., Wang, P., Li, G., Ni, L., & Zhang, Y. (2023). Design of interface circuits and lightweight PUF for TMR sensors. IEEE Sensors Journal, 23(11), 11754-11761.
10. Qu, D., Ma, Y., Yan, J., & Pyrozhenko, M. (2026). TriMeta-BFNet: A Tri-Meta Stacked Atypical-Frequency Bayesian Fourier Neural Network for Hallucination-Resistant Community Detection. Mathematics, 14 (13), 2283.
11. Ma, Y., Qu, D., & Wang, Y. (2026). HALO-GNN: hallucination-resistant temporal graph neural networks for dynamic community detection.Scientific Reports.
12. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2018). Graph attention networks. In Proceedings of ICLR.
13. Hu, H., Yang, Y., Zhao, Q., & Zhang, C. (2022, June). On the role of discount factor in offline reinforcement learning. In International conference on machine learning (pp. 9072-9098). PMLR.
14. Li, X., Wang, P., Li, G., & Zhang, Y. (2023). Design of a novel self-test-on-chip interface ASIC for capacitive accelerometers. IEEE Transactions on Circuits and Systems I: Regular Papers, 70(7), 2834-2843.
15. Wang, Y., Lou, M., Liang, W., & Zhang, C. (2023). Numerical and experimental investigation on tensile fatigue performance of reinforced thermoplastic pipes. Ocean Engineering, 287, 115814.
16. Han, Z., Chen, W., Han, Y., Mao, R., & Qin, J. (2026). Fast Diversified Top-k Rule Discovery via User-Guided Embeddings.IEEE Transactions on Knowledge and Data Engineering.
17. Fang, R., Zhao, J., Wang, S., Pu, R., Li, B., Cai, J., ... & Wang, B. (2025). Saga: Structural aggregation guided alignment with dynamic view and neighborhood order selection for multiview graph domain adaptation. In The Fourteenth International Conference on Learning Representations.
18. Wang, J., & Al Faruque, M. A. (2025, April). Transformer-based contrastive meta-learning for low-resource generalizable activity recognition. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(pp. 1-5). IEEE.
19. Qu, D., & Ma, Y. (2026). Fourier–Transformer Mixer Network for Efficient Video Scene Graph Prediction.Engineering Proceedings,120(1), 16.
20. Li, X., Yang, F., Chen, L., & Cai, H. (2016, July). Saliency transfer: An example-based method for salient object detection. In IJCAI (pp. 3411-3417).
21. Ma, Y., & Qu, D. (2026, February). IMFM-GNN: Interpretable Multimodel GNN Via Fourier-Markov Dynamics for Community Detection in Sustainable Energy Networks. In 2026 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) (pp. 1-6). IEEE.
22. Zhang, W., Huang, M., Zhou, Y., Zhang, J., Yu, J., Wang, J., & Xu, L. (2024). Both2hands: Inferring 3d hands from both text prompts and body dynamics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2393-2404).
23. Lu, J., Fang, Y., Yang, X., Hu, W., Tuo, J., & Zhang, F. (2025). Modal Metamodeling-Based Uncertainty Propagation for Frequency Response in Non-Proportional Damping Systems. International Journal of Structural Stability and Dynamics, 2650279.
24. Yang, Z., Hu, D., Guo, Q., Zuo, L., & Ji, W. (2023). Visual E 2 C: AI-driven visual end-edge-cloud architecture for 6G in low-carbon smart cities. IEEE Wireless Communications, 30(3), 204-210.
25. Gu, C., Zhang, W., Huang, Z., Kou, J., Liu, Z., Zhao, C., Liu, C., Zhang, L., Lin, W., Wang, Z., Deng, J., Xie, Y., Huang, G., Zhang, C., Lu, X., Wang, C., Zhang, Z., Yuan, H., Duan, X., & Fang, Y. (2024). LENS: Layers of evaluation of hallucination in GenAI systems. In 2024 7th International Conference on Universal Village (UV) (pp. 1–85). IEEE. https://doi.org/10.1109/UV63228.2024.11189150
26. Huang, H., Zhang, J., Zhang, J., Xu, J., & Wu, Q. (2020). Low-rank pairwise alignment bilinear network for few-shot fine-grained image classification.IEEE Transactions on Multimedia,23, 1666-1680.
27. Zhou, L., Wang, M., Chen, Q., & Han, H. (2026). SCGRN: Spatiotemporal causal graph reasoning network for regional economic development modeling.Information Processing & Management,63(6), 104761.
28. Peng, Qucheng, et al. "3d vision-language gaussian splatting." International Conference on Learning Representations. Vol. 2025. 2025.
29. Zhang, W., & Pei, S. (2026). Predictive prefetching for retrieval-augmented generation. Proceedings of Machine Learning Research. The Forty-third International Conference on Machine Learning, ICML 2026. https://par.nsf.gov/biblio/10686293
30. Zhang, Y., Zhang, Y., Zhao, M., Feng, S., & Cheung, Y. M. (2026). How to Ask the AI: A User Perspective Survey for Large Language Model Prompting.IEEE Transactions on Artificial Intelligence.
31. Lee, Y., Xu, Y., Gao, P., & Chen, J. (2024). Tenet: triple-enhancement based graph neural network for cell-cell interaction network reconstruction from spatial transcriptomics. Journal of Molecular Biology, 436 (9), 168543.
32. Wang, S., Zhang, X., Wang, P., & Li, X. (2026). Design of Magnetic Sensor Array-Based PUFs for IoT Security. IEEE Transactions on Instrumentation and Measurement, 75, 1-9.
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