Spectral properties of the Laplacian of multiplex networks
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Abstract (arXiv)
One of the more challenging tasks in the understanding of dynamical properties of models on top of complex networks is to capture the precise role of multiplex topologies. In a recent paper, Gomez et al. [Phys. Rev. Lett. 101, 028701 (2013)] proposed a framework for the study of diffusion processes in such networks. Here, we extend the previous framework to deal with general configurations in several layers of networks, and analyze the behavior of the spectrum of the Laplacian of the full multiplex. We derive an interesting decoupling of the problem that allow us to unravel the role played by the interconnections of the multiplex in the dynamical processes on top of them. Capitalizing on this decoupling we perform an asymptotic analysis that allow us to derive analytical expressions for the full spectrum of eigenvalues. This spectrum is used to gain insight into physical phenomena on top of multiplex, specifically, diffusion processes and synchronizability.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jul 2013.
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Citing papers per year: 2021: 4; 2022: 0; 2023: 0; 2024: 2; 2025: 1.
7 papers.
- 2021Ego-GNNs: Exploiting Ego Structures in Graph Neural Networkscited by 17
- 2021mGNN: Generalizing the Graph Neural Networks to the Multilayer Casecited by 4
- 2025Understanding and Improving Laplacian Positional Encodings For Temporal GNNscited by 3
- 2024Clustering Time-Evolving Networks Using the Spatio-Temporal Graph Laplaciancited by 1
- 2021A Tensor-Based Formulation of Hetero-functional Graph Theorycited by 0
- 2021DynACPD Embedding Algorithm for Prediction Tasks in Dynamic Networkscited by 0
- 2024A tutorial on the dynamic Laplaciancited by 0
- 2025Understanding and Improving Laplacian Positional Encodings For Temporal GNNs2 Jun 2025
- 2024A tutorial on the dynamic Laplacian8 Aug 2024
- 2024Clustering Time-Evolving Networks Using the Spatio-Temporal Graph Laplacian12 Jul 2024
- 2021mGNN: Generalizing the Graph Neural Networks to the Multilayer Case21 Sep 2021
- 2021Ego-GNNs: Exploiting Ego Structures in Graph Neural Networks22 Jul 2021
- 2021DynACPD Embedding Algorithm for Prediction Tasks in Dynamic Networks12 Mar 2021
- 2021A Tensor-Based Formulation of Hetero-functional Graph Theory14 Jan 2021