Distributed Sparsest Cut via Eigenvalue Estimation
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arXiv
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| Format: | Preprint |
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2025
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| author | Maus, Yannic de Vos, Tijn |
| author_facet | Maus, Yannic de Vos, Tijn |
| contents | We give new, improved bounds for approximating the sparsest cut value or in other words the conductance $ϕ$ of a graph in the CONGEST model. As our main result, we present an algorithm running in $O(\log^2 n/ϕ)$ rounds in which every vertex outputs a value $\tilde ϕ$ satisfying $ϕ\le \tilde ϕ\le \sqrt{2.01ϕ}$. In most regimes, our algorithm improves significantly over the previously fastest algorithm for the problem [Chen, Meierhans, Probst Gutenberg, Saranurak; SODA 25]. Additionally, our result generalizes to $k$-way conductance.
We obtain these results, by approximating the eigenvalues of the normalized Laplacian matrix $L:=I-\rm{Deg}^{-1/2}A\rm{Deg}^ {-1/2}$, where, $A$ is the adjacency matrix and $\rm{Deg}$ is the diagonal matrix with the weighted degrees on the diagonal. The previous state of the art sparsest cut algorithm is in the technical realm of expander decompositions. Our algorithms, on the other hand, are relatively simple and easy to implement. At the core, they rely on the well-known power method, which comes down to repeatedly multiplying the Laplacian with a vector. This operation can be performed in a single round in the CONGEST model. All our algorithms apply to weighted, undirected graphs. Our lower bounds apply even in unweighted graphs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19898 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributed Sparsest Cut via Eigenvalue Estimation Maus, Yannic de Vos, Tijn Data Structures and Algorithms We give new, improved bounds for approximating the sparsest cut value or in other words the conductance $ϕ$ of a graph in the CONGEST model. As our main result, we present an algorithm running in $O(\log^2 n/ϕ)$ rounds in which every vertex outputs a value $\tilde ϕ$ satisfying $ϕ\le \tilde ϕ\le \sqrt{2.01ϕ}$. In most regimes, our algorithm improves significantly over the previously fastest algorithm for the problem [Chen, Meierhans, Probst Gutenberg, Saranurak; SODA 25]. Additionally, our result generalizes to $k$-way conductance. We obtain these results, by approximating the eigenvalues of the normalized Laplacian matrix $L:=I-\rm{Deg}^{-1/2}A\rm{Deg}^ {-1/2}$, where, $A$ is the adjacency matrix and $\rm{Deg}$ is the diagonal matrix with the weighted degrees on the diagonal. The previous state of the art sparsest cut algorithm is in the technical realm of expander decompositions. Our algorithms, on the other hand, are relatively simple and easy to implement. At the core, they rely on the well-known power method, which comes down to repeatedly multiplying the Laplacian with a vector. This operation can be performed in a single round in the CONGEST model. All our algorithms apply to weighted, undirected graphs. Our lower bounds apply even in unweighted graphs. |
| title | Distributed Sparsest Cut via Eigenvalue Estimation |
| topic | Data Structures and Algorithms |
| url | https://arxiv.org/abs/2508.19898 |