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

2026-05-27

AI Summary

Existing approaches struggle to effectively model the complex cross-layer dependencies in multilayer networks, often treating layers independently or aggregating them in an oversimplified manner. To address this limitation, this work proposes the T-GINEE framework, which explicitly captures inter-layer structural associations and statistical correlations through tensor generalized estimating equations. The method leverages CP tensor decomposition to extract shared latent factors, incorporates a working covariance matrix to model cross-layer dependencies, and employs a tunable link function to accommodate practical characteristics such as sparsity. Theoretical analysis establishes the consistency and asymptotic normality of the resulting estimators. Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of the proposed approach.

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.

Problem

• multilayer networks
• inter-layer dependencies
• tensor decomposition
• network analysis
• cross-network correlations

Innovation

• tensor decomposition
• multilayer graph
• generalized estimating equations
• inter-layer correlation
• statistical regularization