T-GINEE: A Tensor-Based Multilayer Graph Representation Learning

• URL: http://arxiv.org/abs/2605.28300v1
• Date: Wed, 27 May 2026 10:51:29 GMT
• Title: T-GINEE: A Tensor-Based Multilayer Graph Representation Learning
• Abstract summary: T-GINEE is a statistical regularization framework combining tensor-based generalized estimating equations with task-specific loss to model cross-network correlations explicitly.<n>Our theoretical analysis establishes consistency and normality under mild conditions.<n>Extensive experiments on synthetic and real-world datasets validate T-GINEE's effectiveness for multilayer network analysis.
• Score: 50.294562905744215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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