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[2602.15920] Including Node Textual Metadata in Laplacian-constrained Gaussian Graphical Models
Summary
This paper presents a novel approach to graph learning in Gaussian Graphical Models (GGMs) by incorporating node textual metadata, enhancing clustering performance through a new optimization algorithm.
Why It Matters
Incorporating textual metadata into graph learning processes can significantly improve the accuracy and effectiveness of clustering algorithms. This research addresses a gap in traditional methods that often overlook valuable auxiliary information, making it relevant for advancements in machine learning and data analysis.
Key Takeaways
- The proposed method integrates node textual metadata with traditional graph signals.
- An efficient majorization-minimization algorithm is developed for optimization.
- Experimental results show improved clustering performance over existing methods.
- The approach highlights the importance of fusing multiple data sources in machine learning.
- This research can influence future developments in graph-based learning techniques.
Statistics > Machine Learning arXiv:2602.15920 (stat) [Submitted on 17 Feb 2026] Title:Including Node Textual Metadata in Laplacian-constrained Gaussian Graphical Models Authors:Jianhua Wang, Killian Cressant, Pedro Braconnot Velloso, Arnaud Breloy View a PDF of the paper titled Including Node Textual Metadata in Laplacian-constrained Gaussian Graphical Models, by Jianhua Wang and 3 other authors View PDF HTML (experimental) Abstract: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. Comments: Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Signal Processing (eess.SP) Cite as: arXiv:2602.15920 [stat.ML] (or arXiv:2602.15920v1 [stat.ML] f...