Quiver Signal Processing (QSP)
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Abstract (arXiv)
In this paper we state the basics for a signal processing framework on quiver representations. A quiver is a directed graph and a quiver representation is an assignment of vector spaces to the nodes of the graph and of linear maps between the vector spaces associated to the nodes. Leveraging the tools from representation theory, we propose a signal processing framework that allows us to handle heterogeneous multidimensional information in networks. We provide a set of examples where this framework provides a natural set of tools to understand apparently hidden structure in information. We remark that the proposed framework states the basis for building graph neural networks where information can be processed and handled in alternative ways.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 1.
4 papers.
- 2022Causal Fourier Analysis on Directed Acyclic Graphs and Posetscited by 12
- 2021Convolutional Filtering and Neural Networks with Non Commutative Algebrascited by 8
- 2022Learning with Multigraph Convolutional Filterscited by 5
- 2024Quiver Laplacians and Feature Selectioncited by 2
- 2024Quiver Laplacians and Feature Selection10 Apr 2024
- 2022Learning with Multigraph Convolutional Filters28 Oct 2022
- 2022Causal Fourier Analysis on Directed Acyclic Graphs and Posets16 Sep 2022
- 2021Convolutional Filtering and Neural Networks with Non Commutative Algebras23 Aug 2021