Simplicial complexes: higher-order spectral dimension and dynamics
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Abstract (arXiv)
Simplicial complexes constitute the underlying topology of interacting complex systems including among the others brain and social interaction networks. They are generalized network structures that allow to go beyond the framework of pairwise interactions and to capture the many-body interactions between two or more nodes strongly affecting dynamical processes. In fact, the simplicial complexes topology allows to assign a dynamical variable not only to the nodes of the interacting complex systems but also to links, triangles, and so on. Here we show evidence that the dynamics defined on simplices of different dimensions can be significantly different even if we compare dynamics of simplices belonging to the same simplicial complex. By investigating the spectral properties of the simplicial complex model called "Network Geometry with Flavor" we provide evidence that the up and down higher-order Laplacians can have a finite spectral dimension whose value increases as the order of the Laplacian increases. Finally we discuss the implications of this result for higher-order diffusion defined on simplicial complexes.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Jan 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 4; 2025: 1.
9 papers.
- 2021Signal processing on simplicial complexescited by 12
- 2023Dirac signal processing of higher-order topological signalscited by 9
- 2023Homological Neural Networks: A Sparse Architecture for Multivariate Complexitycited by 5
- 2023Homological Convolutional Neural Networkscited by 4
- 2024SPHINX: Structural Prediction using Hypergraph Inference Networkcited by 3
- 2024Dirac-Equation Signal Processing: Physics Boosts Topological Machine Learningcited by 1
- 2025Chordless cycle filtrations for dimensionality detection in complex networks via topological data analysiscited by 1
- 2024Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modelingcited by 0
- 2024A cohomology-based Gromov-Hausdorff metric approach for quantifying molecular similaritycited by 0
- 2025Chordless cycle filtrations for dimensionality detection in complex networks via topological data analysis10 Sep 2025
- 2024Dirac-Equation Signal Processing: Physics Boosts Topological Machine Learning6 Dec 2024
- 2024A cohomology-based Gromov-Hausdorff metric approach for quantifying molecular similarity21 Nov 2024
- 2024SPHINX: Structural Prediction using Hypergraph Inference Network4 Oct 2024
- 2024Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling18 Mar 2024
- 2023Homological Convolutional Neural Networks26 Aug 2023
- 2023Homological Neural Networks: A Sparse Architecture for Multivariate Complexity27 Jun 2023
- 2023Dirac signal processing of higher-order topological signals12 Jan 2023
- 2021Signal processing on simplicial complexes14 Jun 2021