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.
Cite
@article{arxiv.2605.28300,
title = {T-GINEE: A Tensor-Based Multilayer Graph Representation Learning},
author = {Maolin Wang and Ziting Mai and Xuhui Chen and Zhiqi Li and Tianshuo Wei and Yutian Xiao and Wenlin Zhang and Wanyu Wang and Ruocheng Guo and Haoxuan Li and Zenglin Xu and Xiangyu Zhao},
journal= {arXiv preprint arXiv:2605.28300},
year = {2026}
}
Comments
Accepted by ICML 2026