Estimation of Laplacian spectra of direct and strong product graphs
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Abstract (arXiv)
Calculating a product of multiple graphs has been studied in mathematics, engineering, computer science, and more recently in network science, particularly in the context of multilayer networks. One of the important questions to be addressed in this area is how to characterize spectral properties of a product graph using those of its factor graphs. While several such characterizations have already been obtained analytically (mostly for adjacency spectra), characterization of Laplacian spectra of direct product and strong product graphs has remained an open problem. Here we develop practical methods to estimate Laplacian spectra of direct and strong product graphs from spectral properties of their factor graphs using a few heuristic assumptions. Numerical experiments showed that the proposed methods produced reasonable estimation with percentage errors confined within a +/-10% range for most eigenvalues.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jul 2015.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1.
4 papers.
- 2023Scalable Multi-agent Covering Option Discovery based on Kronecker Graphscited by 9
- 2022Learning Multi-agent Skills for Tabular Reinforcement Learning using Factor Graphscited by 4
- 2024Matrix-weighted networks for modeling multidimensional dynamicscited by 0
- 2025Learning Kronecker-Structured Graphs from Smooth Signalscited by 0
- 2025Learning Kronecker-Structured Graphs from Smooth Signals14 May 2025
- 2024Matrix-weighted networks for modeling multidimensional dynamics7 Oct 2024
- 2023Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs21 Jul 2023
- 2022Learning Multi-agent Skills for Tabular Reinforcement Learning using Factor Graphs20 Jan 2022