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We consider the problem of estimating the value of max cut in a graph in the streaming model of computation. At one extreme, there is a trivial $2$-approximation for this problem that uses only $O(\log n)$ space, namely, count the number of…

Data Structures and Algorithms · Computer Science 2014-09-09 Michael Kapralov , Sanjeev Khanna , Madhu Sudan

We devise new cut sparsifiers that are related to the classical sparsification of Nagamochi and Ibaraki [Algorithmica, 1992], which is an algorithm that, given an unweighted graph $G$ on $n$ nodes and a parameter $k$, computes a subgraph…

Data Structures and Algorithms · Computer Science 2021-11-01 Amir Abboud , Robert Krauthgamer , Ohad Trabelsi

The diameter of a graph is one if its most important parameters, being used in many real-word applications. In particular, the diameter dictates how fast information can spread throughout data and communication networks. Thus, it is a…

Data Structures and Algorithms · Computer Science 2019-02-21 Keerti Choudhary , Omer Gold

We consider the problem of estimating the value of MAX-CUT in a graph in the streaming model of computation. At one extreme, there is a trivial $2$-approximation for this problem that uses only $O(\log n)$ space, namely, count the number of…

Data Structures and Algorithms · Computer Science 2018-11-28 Michael Kapralov , Dmitry Krachun

We initiate the study of approximation algorithms and computational barriers for constructing sparse $\alpha$-navigable graphs [IX23, DGM+24], a core primitive underlying recent advances in graph-based nearest neighbor search. Given an…

Data Structures and Algorithms · Computer Science 2025-07-21 Sanjeev Khanna , Ashwin Padaki , Erik Waingarten

We study the following version of cut sparsification. Given a large edge-weighted network $G$ with $k$ terminal vertices, compress it into a smaller network $H$ with the same terminals, such that every minimum terminal cut in $H$…

Data Structures and Algorithms · Computer Science 2019-10-08 Robert Krauthgamer , Havana , Rika

For $\alpha \ge 1$, $\beta \ge 0$, and a graph $G$, a spanning subgraph $H$ of $G$ is said to be an $(\alpha, \beta)$-spanner if $\dist(u, v, H) \le \alpha \cdot \dist(u, v, G) + \beta$ holds for any pair of vertices $u$ and $v$. These type…

Discrete Mathematics · Computer Science 2022-03-17 Prafullkumar Tale

We study the \textsc{Labeled Contractibility} problem, where the input consists of two vertex-labeled graphs $G$ and $H$, and the goal is to determine whether $H$ can be obtained from $G$ via a sequence of edge contractions. Lafond and…

Data Structures and Algorithms · Computer Science 2026-02-12 Yashaswini Mathur , Prafullkumar Tale

For a linear code $\mathcal{C} \subseteq \mathbb{F}_2^n$ and $\alpha \in [0,1]$, call a set $S \subseteq [n]$ an (unweighted) one-sided $\alpha$-sparsifier of $\mathcal{C}$ if for all $c \in \mathcal{C}$, $\mathrm{wt}(c_S)\geq \alpha \cdot…

Combinatorics · Mathematics 2025-09-09 Shayan Oveis Gharan , Arvin Sahami

This paper proposes a scalable algorithmic framework for spectral reduction of large undirected graphs. The proposed method allows computing much smaller graphs while preserving the key spectral (structural) properties of the original…

Data Structures and Algorithms · Computer Science 2018-12-24 Zhiqiang Zhao , Yongyu Wang , Zhuo Feng

We investigate whether an n-vertex instance (G,k) of Treewidth, asking whether the graph G has treewidth at most k, can efficiently be made sparse without changing its answer. By giving a special form of OR-cross-composition, we prove that…

Computational Complexity · Computer Science 2013-08-19 Bart M. P. Jansen

We consider problems of the following type: given a graph $G$, how many edges are needed in the worst case for a sparse subgraph $H$ that approximately preserves distances between a given set of node pairs $P$? Examples include pairwise…

Data Structures and Algorithms · Computer Science 2021-05-10 Greg Bodwin

This work introduces a highly-scalable spectral graph densification framework (SGL) for learning resistor networks with linear measurements, such as node voltages and currents. We show that the proposed graph learning approach is equivalent…

Machine Learning · Computer Science 2023-02-10 Ying Zhang , Zhiqiang Zhao , Zhuo Feng

Motivated by the problem of routing reliably and scalably in a graph, we introduce the notion of a splicer, the union of spanning trees of a graph. We prove that for any bounded-degree n-vertex graph, the union of two random spanning trees…

Discrete Mathematics · Computer Science 2008-07-10 Navin Goyal , Luis Rademacher , Santosh Vempala

We contribute an approach to the problem of locally computing sparse connected subgraphs of dense graphs. In this setting, given an edge in a connected graph $G = (V, E)$, an algorithm locally decides its membership in a sparse connected…

Data Structures and Algorithms · Computer Science 2020-07-13 Rogers Epstein

A \emph{tree cut-sparsifier} $T$ of quality $\alpha$ of a graph $G$ is a single tree that preserves the capacities of all cuts in the graph up to a factor of $\alpha$. A \emph{tree flow-sparsifier} $T$ of quality $\alpha$ guarantees that…

Data Structures and Algorithms · Computer Science 2026-02-17 Daniel Agassy , Dani Dorfman , Haim Kaplan

Given an $H$-minor-free graph $G$ and an integer $k$, our main technical contribution is sampling in randomized polynomial time an induced subgraph $G'$ of $G$ and a tree decomposition of $G'$ of width $\widetilde{O}(k)$ such that for every…

Data Structures and Algorithms · Computer Science 2026-04-01 Dániel Marx , Marcin Pilipczuk , Michał Pilipczuk

Good approximations have been attained for the sparsest cut problem by rounding solutions to convex relaxations via low-distortion metric embeddings. Recently, Bryant and Tupper showed that this approach extends to the hypergraph setting by…

Data Structures and Algorithms · Computer Science 2023-03-09 Adam D. Jozefiak , F. Bruce Shepherd

It is shown that the following holds for each $\varepsilon>0$. For $G$ an $n$-vertex graph of maximum degree $D$ and "lists" $L_v$ ($v \in V(G)$) chosen independently and uniformly from the ($(1+\varepsilon)\ln n$)-subsets of $\{1, ...,…

Combinatorics · Mathematics 2023-06-02 Jeff Kahn , Charles Kenney

Consider a random graph $G$ of size $N$ constructed according to a \textit{graphon} $w \, : \, [0,1]^{2} \mapsto [0,1]$ as follows. First embed $N$ vertices $V = \{v_1, v_2, \ldots, v_N\}$ into the interval $[0,1]$, then for each $i < j$…

Statistics Theory · Mathematics 2021-12-09 Amine Natik , Aaron Smith