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Spectral density approximation methods for sparse graphs: A review
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DOI：10.1016/j.jocs.2026.103009.png)
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Abstract
En 中文
• Reviews approximation methods for spectral density estimation in sparse graphs.
• Analyzes the computational complexity of all reviewed approximation methods.
• Guides method selection by graph structure, sparsity, and required precision.
• Provides an open-source Python library implementing all reviewed methods.
Keywords:
05C50
65F15
68R10
Spectral graph theory
Spectral density
Eigenvalue distribution
Graph spectra
Adjacency matrix
Laplacian matrix
Random graphs
Journal
J
IF:
3.7
Papers:
205
Citations:
0