融合节点文本元数据与信号，提升图模型学习效果
Including Node Textual Metadata in Laplacian-constrained Gaussian Graphical Models
- 用拉普拉斯约束联合优化节点信号与文本元数据
- 在金融数据集上显著优于仅用信号或元数据的方法
- 适合处理含丰富文本信息的图结构数据
本文研究高斯图模型（GGMs）中的图学习问题。实际数据矩阵常包含辅助元数据（如每个节点的文本描述），但传统图估计方法通常忽略这些信息。为弥补这一空白，我们提出一种基于拉普拉斯约束的GGM图学习方法，联合利用节点信号与文本元数据。该方法形成一个优化问题，我们设计了一种高效的重大化-最小化（MM）算法，每轮迭代均有闭式更新。在真实金融数据集上的实验表明，相比仅使用信号或元数据的现有先进方法，所提方法显著提升了图聚类性能，验证了融合两类信息的有效性。
原文摘要 · Abstract (English)
This paper addresses graph learning in Gaussian Graphical Models (GGMs). In this context, data matrices often come with auxiliary metadata (e.g., textual descriptions associated with each node) that is usually ignored in traditional graph estimation processes. To fill this gap, we propose a graph learning approach based on Laplacian-constrained GGMs that jointly leverages the node signals and such metadata. The resulting formulation yields an optimization problem, for which we develop an efficient majorization-minimization (MM) algorithm with closed-form updates at each iteration. Experimental results on a real-world financial dataset demonstrate that the proposed method significantly improves graph clustering performance compared to state-of-the-art approaches that use either signals or metadata alone, thus illustrating the interest of fusing both sources of information.
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