The Laplacian Spectrum of Large Graphs Sampled from Graphons
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Abstract (arXiv)
This paper studies the Laplacian spectrum and the average effective resistance of (large) graphs that are sampled from graphons. Broadly speaking, our main finding is that the Laplacian eigenvalues of a large dense graph can be effectively approximated by using the degree function of the corresponding graphon. More specifically, we show how to approximate the distribution of the Laplacian eigenvalues and the average effective resistance (Kirchhoff index) of the graph. For all cases, we provide explicit bounds on the approximation errors and derive the asymptotic rates at which the errors go to zero when the number of nodes goes to infinity. Our main results are proved under the conditions that the graphon is piecewise Lipschitz and bounded away from zero.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Apr 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 0; 2025: 2.
8 papers.
- 2022G-Mixup: Graph Data Augmentation for Graph Classificationcited by 145
- 2023Does Graph Distillation See Like Vision Dataset Counterpart?cited by 45
- 2021Transferability Properties of Graph Neural Networkscited by 42
- 2023Fine-tuning Graph Neural Networks by Preserving Graph Generative Patternscited by 13
- 2023Laplacian Change Point Detection for Single and Multi-view Dynamic Graphscited by 7
- 2022Stable and Transferable Hyper-Graph Neural Networkscited by 2
- 2025A Generative Model for Controllable Feature Heterophily in Graphscited by 0
- 2025A Spectral Framework for Graph Neural Operators: Convergence Guarantees and Tradeoffscited by 0
- 2025A Spectral Framework for Graph Neural Operators: Convergence Guarantees and Tradeoffs23 Oct 2025
- 2025A Generative Model for Controllable Feature Heterophily in Graphs27 Sep 2025
- 2023Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns21 Dec 2023
- 2023Does Graph Distillation See Like Vision Dataset Counterpart?13 Oct 2023
- 2023Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs2 Feb 2023
- 2022Stable and Transferable Hyper-Graph Neural Networks11 Nov 2022
- 2022G-Mixup: Graph Data Augmentation for Graph Classification15 Feb 2022
- 2021Transferability Properties of Graph Neural Networks9 Dec 2021