The topological Dirac equation of networks and simplicial complexes
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Abstract (arXiv)
We define the topological Dirac equation describing the evolution of a topological wave function on networks or on simplicial complexes. On networks, the topological wave function describes the dynamics of topological signals or cochains, i.e. dynamical signals defined both on nodes and on links. On simplicial complexes the wave function is also defined on higher-dimensional simplices. Therefore the topological wave function satisfies a relaxed condition of locality as it acquires the same value along simplices of dimension larger than zero. The topological Dirac equation defines eigenstates whose dispersion relation is determined by the spectral properties of the Dirac (or chiral) operator defined on networks and generalized network structures including simplicial complexes and multiplex networks. On simplicial complexes the Dirac equation leads to multiple energy bands. On multiplex networks the topological Dirac equation can be generalized to distinguish between different mutlilinks leading to a natural definition of rotations of the topological spinor. The topological Dirac equation is here initially formulated on a spatial network or simplicial complex for describing the evolution of the topological wave function in continuous time. This framework is also extended to treat the topological Dirac equation on 1+d spaces describing a discrete space-time with one temporal dimension and d spatial dimensions with d∈ 1,2,3. This work includes also the discussion of numerical results obtained by implementing the topological Dirac equation on simplicial complex models and on real simple and multiplex network data.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Jun 2021.
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Citing papers per year: 2023: 4; 2024: 2; 2025: 1; 2026: 4.
11 papers.
- 2023Generalized Simplicial Attention Neural Networkscited by 27
- 2023Gaussian Processes on Cellular Complexescited by 15
- 2023Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphscited by 15
- 2023Dirac signal processing of higher-order topological signalscited by 9
- 2026Topological Neural Operatorscited by 4
- 2025Matched Topological Subspace Detectorcited by 3
- 2024Dirac--Bianconi Graph Neural Networks -- Enabling Non-Diffusive Long-Range Graph Predictionscited by 2
- 2024Dirac-Equation Signal Processing: Physics Boosts Topological Machine Learningcited by 1
- 2026Stationarity and Spectral Characterization of Random Signals on Simplicial Complexescited by 1
- 2026Topological Signal Processing: An Application-Oriented Tutorialcited by 0
- 2026Collapsed Effective Operators for Higher-order Structurescited by 0
- 2026Collapsed Effective Operators for Higher-order Structures22 Jun 2026
- 2026Topological Neural Operators8 Jun 2026
- 2026Topological Signal Processing: An Application-Oriented Tutorial18 May 2026
- 2026Stationarity and Spectral Characterization of Random Signals on Simplicial Complexes3 Feb 2026
- 2025Matched Topological Subspace Detector8 Apr 2025
- 2024Dirac-Equation Signal Processing: Physics Boosts Topological Machine Learning6 Dec 2024
- 2024Dirac--Bianconi Graph Neural Networks -- Enabling Non-Diffusive Long-Range Graph Predictions17 Jul 2024
- 2023Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs15 Dec 2023
- 2023Gaussian Processes on Cellular Complexes2 Nov 2023
- 2023Generalized Simplicial Attention Neural Networks5 Sep 2023
- 2023Dirac signal processing of higher-order topological signals12 Jan 2023