The Strong Spectral Property and the Jacobian Method for Weighted Laplacian Matrices
Minerva Catral, Shaun Fallat, Himanshu Gupta, Jephian C.-H. Lin

TL;DR
This paper introduces the strong spectral property for weighted Laplacian matrices, compares the Jacobian method with strong properties, and analyzes spectral regions and algebraic connectivity for small graphs.
Contribution
It defines a new strong property for weighted Laplacian matrices and compares it with the Jacobian method, advancing spectral analysis techniques for graph Laplacians.
Findings
Identified spectral boundaries for weighted Laplacian matrices of 4-vertex graphs.
Established the existence of strong weighted Laplacian matrices for certain graph families.
Extended analysis of spectral regions beyond previous work.
Abstract
Strong matrix properties, roughly speaking, refer to generic conditions on a matrix such that its spectral perturbation and pattern perturbation interact nicely to cover a neighborhood in the ambient space. With a rich history, these strong properties originate from various fields, including the inverse eigenvalue problem, the sign pattern problem, and structural graph theory. In this paper, we introduce a new strong property, the strong spectral property for weighted Laplacian matrices (SSPWL), and establish the corresponding Supergraph and Bifurcation lemmas. Instead of the space of symmetric matrices, the SSPWL considers the ambient space spanned by all weighted Laplacian matrices. Moreover, we provide a detailed study comparing the Jacobian Method and some strong properties, leading to a full understanding between these two techniques used in different problems. Using these tools,…
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Taxonomy
TopicsMatrix Theory and Algorithms · Graph theory and applications · Stability and Control of Uncertain Systems
