T-GINEE: A Tensor-Based Multilayer Graph Representation Learning
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Computer Science > Machine Learning
Title:T-GINEE: A Tensor-Based Multilayer Graph Representation Learning
Abstract: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.
| Comments: | Accepted by ICML 2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.28300 [cs.LG] |
| (or arXiv:2605.28300v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.28300
arXiv-issued DOI via DataCite (pending registration)
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