The Graphon Fourier Transform
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Abstract (arXiv)
In many network problems, graphs may change by the addition of nodes, or the same problem may need to be solved in multiple similar graphs. This generates inefficiency, as analyses and systems that are not transferable have to be redesigned. To address this, we consider graphons, which are both limit objects of convergent graph sequences and random graph models. We define graphon signals and introduce the Graphon Fourier Transform (WFT), to which the Graph Fourier Transform (GFT) is shown to converge. This result is demonstrated in two numerical experiments where, as expected, the GFT converges, hinting to the possibility of centralizing analysis and design on graphons to leverage transferability.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Oct 2019.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 4; 2024: 0; 2025: 1; 2026: 1.
10 papers.
- 2023Does Graph Distillation See Like Vision Dataset Counterpart?cited by 45
- 2021Transferability Properties of Graph Neural Networkscited by 42
- 2021Learning by Transference: Training Graph Neural Networks on Growing Graphscited by 22
- 2022Training Graph Neural Networks on Growing Stochastic Graphscited by 5
- 2021Learning Graphon Autoencoders for Generative Graph Modelingcited by 4
- 2023A Poincar'e Inequality and Consistency Results for Signal Sampling on Large Graphscited by 1
- 2023Generalized Graphon Process: Convergence of Graph Frequencies in Stretched Cut Distancecited by 0
- 2023Spectral Convergence of Complexon Shift Operatorscited by 0
- 2025Graph Semi-Supervised Learning for Point Classification on Data Manifoldscited by 0
- 2026Fundamental Limits of Transferability and Equivariance in Algebraic Signal Models I: Finite Dimensionscited by 0
- 2026Fundamental Limits of Transferability and Equivariance in Algebraic Signal Models I: Finite Dimensions28 Sep 2026
- 2025Graph Semi-Supervised Learning for Point Classification on Data Manifolds13 Jun 2025
- 2023A Poincar'e Inequality and Consistency Results for Signal Sampling on Large Graphs17 Nov 2023
- 2023Does Graph Distillation See Like Vision Dataset Counterpart?13 Oct 2023
- 2023Spectral Convergence of Complexon Shift Operators12 Sep 2023
- 2023Generalized Graphon Process: Convergence of Graph Frequencies in Stretched Cut Distance11 Sep 2023
- 2022Training Graph Neural Networks on Growing Stochastic Graphs27 Oct 2022
- 2021Transferability Properties of Graph Neural Networks9 Dec 2021
- 2021Learning by Transference: Training Graph Neural Networks on Growing Graphs7 Jun 2021
- 2021Learning Graphon Autoencoders for Generative Graph Modeling29 May 2021