Vertex-frequency analysis on graphs
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VFC is an adaptation of the signal-biased spectral clustering method proposed in ref. 23.
“This method, which we call VFC, is an adaptation of the signal-biased spectral clustering proposed in ref.23.”
From Quantifying the effect of experimental perturbations at single-cell resolution (2021) · cites this paper as [23]
VFC provides a feature basis for clustering based on the spectrogram of sample indicator signals.
“The VFC algorithm provides a feature basis for clustering based on the spectrogram23 of the sample indicator signals, which can be thought of as a histogram of frequency components of graph signals.”
From Quantifying the effect of experimental perturbations at single-cell resolution (2021) · cites this paper as [23]
Translating the normalized heat kernel, multiplying it element-wise with the target signal, and taking the Fourier transform yields a WGFT of f that is localized according to diffusion distance from each vertex to every other vertex.
“By translating this kernel, element-wise multiplying it with our target signal f and taking the Fourier transform of the result, we obtain a WGFT of f that is localized based on the diffusion distance23,57 from each vertex to every other vertex in the graph.”
From Quantifying the effect of experimental perturbations at single-cell resolution (2021) · cites this paper as [23]
Recent work in graph signal processing has extended the construction of the windowed Fourier transform to graph signals.
“Recent works in GSP have generalized the construction of WFT to graph signals23.”
From Quantifying the effect of experimental perturbations at single-cell resolution (2021) · cites this paper as [23]
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References 46 · Cited by 349
Research works printed but not linked: 2
- D.I Shuman, B. Ricaud, P. Vandergheynst, A windowed graph Fourier tranform, in: Proc. IEEE Stat. Signal Process. Wkshp, Ann Arbor, MI, 2012, pp. 133-136.
- L.J. Grady, J.R. Polimeni, Discrete Calculus, Springer, 2010.
- Authors
- Shuman, D. I., Ricaud, B., & Vandergheynst, P.
- Affiliations
- Macalester College (US)
- École Polytechnique Fédérale de Lausanne (CH)
- Corresponding author
- Shuman, David I
- Venue
- Applied and Computational Harmonic Analysis
- Publisher
- Elsevier BV
- Published
- 2016-03
- Volume
- 40
- Issue
- 2
- Pages
- 260-291
- ISSN
- 1063-5203 (ISSN-L)
- OpenAlex
- W2016423476
- Cited by
- 349
- References
- 46
- Citations by year
- 2026: 15 · 2025: 24 · 2024: 32 · 2023: 25 · 2022: 18 · 2021: 42 · 2020: 49 · 2019: 28 · 2018: 35 · 2017: 32 · 2016: 27 · 2015: 19 · 2014: 3
- FWCI
- 73.23
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Applied and Computational Harmonic Analysis (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- FET-Open (255931)
- Topics
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical Analysis and Transform Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Engineering › Computational Mechanics › Sparse and Compressive Sensing Techniques
- Keywords
- graph convolution, graph kernels, weighted graphs
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