Higher-order signal processing with the Dirac operator
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Abstract (arXiv)
The processing of signals on simplicial and cellular complexes defined by nodes, edges, and higher-order cells has recently emerged as a principled extension of graph signal processing for signals supported on more general topological spaces. However, most works so far have considered signal processing problems for signals associated to only a single type of cell such as the processing of node signals, or edge signals, by considering an appropriately defined shift operator, like the graph Laplacian or the Hodge Laplacian. Here we introduce the Dirac operator as a novel kind of shift operator for signal processing on complexes. We discuss how the Dirac operator has close relations but is distinct from the Hodge-Laplacian and examine its spectral properties. Importantly, the Dirac operator couples signals defined on cells of neighboring dimensions in a principled fashion. We demonstrate how this enables us, e.g., to leverage node signals for the processing of edge flows.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Dec 2022.
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Citing papers per year: 2023: 3; 2024: 1; 2025: 2; 2026: 2.
8 papers.
- 2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 66
- 2023Generalized Simplicial Attention Neural Networkscited by 27
- 2023Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphscited by 15
- 2025Copresheaf Topological Neural Networks: A Generalized Deep Learning Frameworkcited by 12
- 2023Dirac signal processing of higher-order topological signalscited by 9
- 2026Topological Neural Operatorscited by 4
- 2025Matched Topological Subspace Detectorcited by 3
- 2026Stationarity and Spectral Characterization of Random Signals on Simplicial Complexescited by 1
- 2026Topological Neural Operators8 Jun 2026
- 2026Stationarity and Spectral Characterization of Random Signals on Simplicial Complexes3 Feb 2026
- 2025Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework27 May 2025
- 2025Matched Topological Subspace Detector8 Apr 2025
- 2024Position: Topological Deep Learning is the New Frontier for Relational Learning14 Feb 2024
- 2023Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs15 Dec 2023
- 2023Generalized Simplicial Attention Neural Networks5 Sep 2023
- 2023Dirac signal processing of higher-order topological signals12 Jan 2023