Near-linear Size Hypergraph Cut Sparsifiers
Yu Chen, Sanjeev Khanna, Ansh Nagda
Abstract
Cuts in graphs are a fundamental object of study, and play a central role in the study of graph algorithms. The problem of sparsifying a graph while approximately preserving its cut structure has been extensively studied and has many applications. In a seminal work, Benczúr and Karger (1996) showed that given any n-vertex undirected weighted graph G and a parameter ε ∈ (0,1), there is a near-linear time algorithm that outputs a weighted subgraph G' of G of size Õ(n/ε2) such that the weight of every cut in G is preserved to within a ( 1±ε)-factor in G'. The graph G' is referred to as a ( 1±ε)-approximate cut sparsifier of G. A natural question is if such cut-preserving sparsifiers also exist for hypergraphs. Kogan and Krauthgamer (2015) initiated a study of this question and showed that given any weighted hypergraph H where the cardinality of each hyperedge is bounded by r, there is a polynomial-time algorithm to find a ( 1±ε)-approximate cut sparsifier of H of size Õ([nr/(ε2)]). Since r can be as large as n, in general, this gives a hypergraph cut sparsifier of size Õ(n2/ε2), which is a factor n larger than the Benczúr-Karger bound for graphs. It has been an open question whether or not Benczúr-Karger bound is achievable on hypergraphs. In this work, we resolve this question in the affirmative by giving a new polynomial-time algorithm for creating hypergraph sparsifiers of size Õ(n/ε2).
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 157a55ae-e691-42ae-b3b1-57d878caab0eCited by top-tier papers18
- On the Range Avoidance Problem for CircuitsHanlin Ren, Rahul Santhanam, Zhikun WangFOCS 2022 · 19 citations
- Towards tight bounds for spectral sparsification of hypergraphsMichael Kapralov, Robert Krauthgamer, Jakab Tardos, Yuichi YoshidaSTOC 2021 · 18 citations
- Spectral Hypergraph Sparsifiers of Nearly Linear SizeMichael Kapralov, Robert Krauthgamer, Jakab Tardos, Yuichi YoshidaFOCS 2021 · 14 citations
- Efficient Algorithms and New Characterizations for CSP SparsificationSanjeev Khanna, Aaron Putterman, Madhu SudanSTOC 2025 · 12 citations
- Sparsification of Decomposable Submodular FunctionsAkbar Rafiey, Yuichi YoshidaAAAI 2022 · 11 citations
Builds on1
Related papers
- Chaining, Group Leverage Score Overestimates, and Fast Spectral Hypergraph SparsificationArun Jambulapati, Yang P. Liu, Aaron SidfordSTOC 2023 · 8 citations
- Quotient sparsification for submodular functionsKent QuanrudSODA 2024 · 4 citations
- Quantum Speedup for Hypergraph SparsificationChenghua Liu, Minbo Gao, Zhengfeng Ji, Mingsheng YingICML 2025
- Near-Optimal Size Linear Sketches for Hypergraph Cut SparsifiersSanjeev Khanna, Aaron Putterman, Madhu SudanFOCS 2024 · 2 citations
- Spectral Hypergraph Sparsification via ChainingJames R. LeeSTOC 2023 · 7 citations
