T-GINEE: A Tensor-Based Multilayer Graph Representation 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 ...
| Authors | Maolin Wang, Ziting Mai, Xuhui Chen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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