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In this paper we show how to find nearly optimal embeddings of large trees in several natural classes of graphs. The size of the tree T can be as large as a constant fraction of the size of the graph G, and the maximum degree of T can be…

Combinatorics · Mathematics 2007-07-17 Benny Sudakov , Jan Vondrak

A graph $G=(V,E)$ is geometrically embeddable into a normed space $X$ when there is a mapping $\zeta: V\to X$ such that $\|\zeta(v)-\zeta(w)\|_X\leqslant 1$ if and only if $\{v,w\}\in E$, for all distinct $v,w\in V$. Our result is the…

Combinatorics · Mathematics 2026-04-20 Dylan J. Altschuler , Pandelis Dodos , Konstantin Tikhomirov , Konstantinos Tyros

Metric embedding has become a common technique in the design of algorithms. Its applicability is often dependent on how high the embedding's distortion is. For example, embedding finite metric space into trees may require linear distortion…

Data Structures and Algorithms · Computer Science 2007-05-23 Yair Bartal , Manor Mendel

Embeddings of graphs into distributions of trees that preserve distances in expectation are a cornerstone of many optimization algorithms. Unfortunately, online or dynamic algorithms which use these embeddings seem inherently randomized and…

Data Structures and Algorithms · Computer Science 2021-02-11 Bernhard Haeupler , D Ellis Hershkowitz , Goran Zuzic

Metric embeddings into structured spaces, particularly hierarchically well-separated trees (HSTs), are a fundamental tool in the design of online algorithms. In the classical online embedding setting, points arrive sequentially and must be…

Data Structures and Algorithms · Computer Science 2026-05-13 Christian Coester , Yichen Huang

The largest common embeddable subtree problem asks for the largest possible tree embeddable into two input trees and generalizes the classical maximum common subtree problem. Several variants of the problem in labeled and unlabeled rooted…

Data Structures and Algorithms · Computer Science 2018-05-03 Andre Droschinsky , Nils M. Kriege , Petra Mutzel

Tree embedding has been a fundamental method in algorithm design with wide applications. We focus on the efficiency of building tree embedding in various computational settings under high-dimensional Euclidean $\mathbb{R}^d$. We devise a…

Data Structures and Algorithms · Computer Science 2026-01-13 Gramoz Goranci , Shaofeng H. -C. Jiang , Peter Kiss , Qihao Kong , Yi Qian , Eva Szilagyi

Maximum diversity aims at selecting a diverse set of high-quality objects from a collection, which is a fundamental problem and has a wide range of applications, e.g., in Web search. Diversity under a uniform or partition matroid constraint…

Data Structures and Algorithms · Computer Science 2021-04-13 Guangyi Zhang , Aristides Gionis

We prove optimal bounds for the convergence rate of ordinal embedding (also known as non-metric multidimensional scaling) in the 1-dimensional case. The examples witnessing optimality of our bounds arise from a result in additive number…

Statistics Theory · Mathematics 2019-05-01 Jordan S. Ellenberg , Lalit Jain

In this paper we investigate the problem of estimating the cluster tree for a density $f$ supported on or near a smooth $d$-dimensional manifold $M$ isometrically embedded in $\mathbb{R}^D$. We analyze a modified version of a $k$-nearest…

Clustering is a fundamental unsupervised learning approach. Many clustering algorithms -- such as $k$-means -- rely on the euclidean distance as a similarity measure, which is often not the most relevant metric for high dimensional data…

Machine Learning · Computer Science 2019-10-22 Aude Genevay , Gabriel Dulac-Arnold , Jean-Philippe Vert

A very popular class of models for networks posits that each node is represented by a point in a continuous latent space, and that the probability of an edge between nodes is a decreasing function of the distance between them in this latent…

Statistics Theory · Mathematics 2025-01-07 Cosma Rohilla Shalizi , Dena Marie Asta

This paper addresses the basic question of how well can a tree approximate distances of a metric space or a graph. Given a graph, the problem of constructing a spanning tree in a graph which strongly preserves distances in the graph is a…

Discrete Mathematics · Computer Science 2016-08-31 Ittai Abraham , Yair Bartal , Ofer Neiman

We prove that if a tree $T$ has $n$ vertices and maximum degree at most $\Delta$, then a copy of $T$ can almost surely be found in the random graph $\mathcal{G}(n,\Delta\log^5 n/n)$.

Combinatorics · Mathematics 2014-06-27 Richard Montgomery

This paper shows that graph spectral embedding using the random walk Laplacian produces vector representations which are completely corrected for node degree. Under a generalised random dot product graph, the embedding provides uniformly…

Methodology · Statistics 2021-05-05 Alexander Modell , Patrick Rubin-Delanchy

We derive a sufficient condition for a sparse graph G on n vertices to contain a copy of a tree T of maximum degree at most d on (1-\epsilon)n vertices, in terms of the expansion properties of G. As a result we show that for fixed d\geq 2…

Combinatorics · Mathematics 2007-06-29 Noga Alon , Michael Krivelevich , Benny Sudakov

It is known that every graph with n vertices embeds stochastically into trees with distortion $O(\log n)$. In this paper, we show that this upper bound is sharp for a large class of graphs. As this class of graphs contains diamond graphs,…

Combinatorics · Mathematics 2023-06-13 Th. Schlumprecht , Garrett Tresch

We propose the following conjecture: For every fixed $\alpha\in [0,\frac 13)$, each graph of minimum degree at least $(1+\alpha)\frac k2$ and maximum degree at least $2(1-\alpha)k$ contains each tree with $k$ edges as a subgraph. Our main…

Combinatorics · Mathematics 2020-08-13 Guido Besomi , Matías Pavez-Signé , Maya Stein

The maximum common subtree isomorphism problem asks for the largest possible isomorphism between subtrees of two given input trees. This problem is a natural restriction of the maximum common subgraph problem, which is ${\sf NP}$-hard in…

Data Structures and Algorithms · Computer Science 2016-08-23 Andre Droschinsky , Nils M. Kriege , Petra Mutzel

We study the problem of partitioning a set of $n$ objects in a metric space into $k$ clusters $V_1,\dots,V_k$. The quality of the clustering is measured by considering the vector of cluster costs and then minimizing some monotone symmetric…

Data Structures and Algorithms · Computer Science 2025-01-10 Matthias Kaul , Kelin Luo , Matthias Mnich , Heiko Röglin
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