提出新模型显式建模多层网络间复杂关联，提升分析精度。
T-GINEE: A Tensor-Based Multilayer Graph Representation Learning
- 用张量分解捕捉多层网络共享潜在结构
- 通过工作协方差矩阵建模层间相关性，效果优于独立处理
- 支持稀疏等特性，适合真实多层网络分析
传统网络分析聚焦单层网络，但现实系统常表现为含多种关系类型的多层网络。现有方法通常将各层独立处理或简单聚合，难以捕捉复杂的层间依赖。为此，我们提出 T-GINEE（基于张量的多层图估计方程），一种结合张量广义估计方程与任务特定损失的统计正则化框架，显式建模跨网络相关性。关键创新包括：(1) 使用 CP 张量分解通过共享潜因子捕捉结构依赖；(2) 采用广义估计方程框架，利用工作协方差矩阵建模层间相关性；(3) 设计灵活链接函数以适应稀疏等特性。理论分析证明在弱条件下具有一致性和渐近正态性。在合成与真实数据集上的大量实验验证了 T-GINEE 在多层网络分析中的有效性。
原文摘要 · Abstract (English)
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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