Including Node Textual Metadata in Laplacian-constrained Gaussian Graphical Models
This addresses graph learning for domains with auxiliary metadata (e.g., finance), though it appears incremental as it extends existing Laplacian-constrained GGMs.
The paper tackled graph learning in Gaussian Graphical Models by incorporating node textual metadata alongside node signals, developing a Laplacian-constrained approach with an efficient optimization algorithm. Experimental results on a financial dataset showed significant improvements in graph clustering performance compared to methods using only signals or metadata alone.
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.