T-GINEE: A Tensor-Based Multilayer Graph Representation Learning
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918527732023296 |
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| author | Wang, Maolin Mai, Ziting Chen, Xuhui Li, Zhiqi Wei, Tianshuo Xiao, Yutian Zhang, Wenlin Wang, Wanyu Guo, Ruocheng Li, Haoxuan Xu, Zenglin Zhao, Xiangyu |
| author_facet | Wang, Maolin Mai, Ziting Chen, Xuhui Li, Zhiqi Wei, Tianshuo Xiao, Yutian Zhang, Wenlin Wang, Wanyu Guo, Ruocheng Li, Haoxuan Xu, Zenglin Zhao, Xiangyu |
| contents | Traditional network analysis focuses on single-layer networks, real-world systems often form multilayer networks with multiple relationship types. However, existing methods typically fail to capture complex inter-layer dependencies by treating layers independently or aggregating them. To address this, we propose T-GINEE (Tensor-Based Generalized Multilayer-graph Estimating Equation), a statistical regularization framework combining tensor-based generalized estimating equations with task-specific loss to model cross-network correlations explicitly. Key innovations include: (1) CP tensor decomposition capturing structural dependencies via shared latent factors; (2) a generalized estimating equation framework modeling inter-layer correlations through working covariance matrices; and (3) a flexible link function accommodating characteristics like sparsity. Our theoretical analysis establishes consistency and asymptotic normality under mild conditions. Extensive experiments on synthetic and real-world datasets validate T-GINEE's effectiveness for multilayer network analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_28300 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | T-GINEE: A Tensor-Based Multilayer Graph Representation Learning Wang, Maolin Mai, Ziting Chen, Xuhui Li, Zhiqi Wei, Tianshuo Xiao, Yutian Zhang, Wenlin Wang, Wanyu Guo, Ruocheng Li, Haoxuan Xu, Zenglin Zhao, Xiangyu Machine Learning Traditional network analysis focuses on single-layer networks, real-world systems often form multilayer networks with multiple relationship types. However, existing methods typically fail to capture complex inter-layer dependencies by treating layers independently or aggregating them. To address this, we propose T-GINEE (Tensor-Based Generalized Multilayer-graph Estimating Equation), a statistical regularization framework combining tensor-based generalized estimating equations with task-specific loss to model cross-network correlations explicitly. Key innovations include: (1) CP tensor decomposition capturing structural dependencies via shared latent factors; (2) a generalized estimating equation framework modeling inter-layer correlations through working covariance matrices; and (3) a flexible link function accommodating characteristics like sparsity. Our theoretical analysis establishes consistency and asymptotic normality under mild conditions. Extensive experiments on synthetic and real-world datasets validate T-GINEE's effectiveness for multilayer network analysis. |
| title | T-GINEE: A Tensor-Based Multilayer Graph Representation Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.28300 |