mlsanityData to 14 Oct 2026

Finite Impulse Response Filters for Simplicial Complexes

arXiv 2103.12587 ↗First posted 23 Mar 2021cs.SI cs.LG cs.SYcited by 15

Not on the map. Why?

Abstract (arXiv)

In this paper, we study linear filters to process signals defined on simplicial complexes, i.e., signals defined on nodes, edges, triangles, etc. of a simplicial complex, thereby generalizing filtering operations for graph signals. We propose a finite impulse response filter based on the Hodge Laplacian, and demonstrate how this filter can be designed to amplify or attenuate certain spectral components of simplicial signals. Specifically, we discuss how, unlike in the case of node signals, the Fourier transform in the context of edge signals can be understood in terms of two orthogonal subspaces corresponding to the gradient-flow signals and curl-flow signals arising from the Hodge decomposition. By assigning different filter coefficients to the associated terms of the Hodge Laplacian, we develop a subspace-varying filter which enables more nuanced control over these signal types. Numerical experiments are conducted to show the potential of simplicial filters for sub-component extraction, denoising and model approximation.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 23 Mar 2021.

Builds on

12 of 22 references.

References · 12

  1. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 413
  2. 2019Topological Signal Processing over Simplicial Complexescited by 96
  3. 2018Random Walks on Simplicial Complexes and the normalized Hodge 1-Laplaciancited by 86
  4. 2021Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyondcited by 82
  5. 2020Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networkscited by 75
  6. 2013Discrete Signal Processing on Graphs: Frequency Analysiscited by 73
  7. 2019HodgeNet: Graph Neural Networks for Edge Datacited by 39
  8. 2018Flow Smoothing and Denoising: Graph Signal Processing in the Edge-Spacecited by 37
  9. 2015Discrete Signal Processing on Graphs: Sampling Theorycited by 34
  10. 2019Graph-based Semi-Supervised & Active Learning for Edge Flowscited by 25
  11. 2019Introducing Hypergraph Signal Processing: Theoretical Foundation and Practical Applicationscited by 20
  12. 2015Metrics in the Space of High Order Networkscited by 2

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Cited by year

Citing papers per year: 2021: 3; 2022: 5; 2023: 7.

15 papers.

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