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Asymptotic properties of random graph sequences, like occurrence of a giant component or full connectivity in Erd\H{o}s-R\'enyi graphs, are usually derived with very specific choices for defining parameters. The question arises to which…

Probability · Mathematics 2024-02-20 B. J. K. Kleijn , S. Rizzelli

We study the two inference problems of detecting and recovering an isolated community of \emph{general} structure planted in a random graph. The detection problem is formalized as a hypothesis testing problem, where under the null…

Data Structures and Algorithms · Computer Science 2022-01-25 Wasim Huleihel

We investigate the asymptotic structure of a random perfect graph $P_n$ sampled uniformly from the perfect graphs on vertex set $\{1,\ldots,n\}$. Our approach is based on the result of Pr\"omel and Steger that almost all perfect graphs are…

Combinatorics · Mathematics 2017-09-07 Colin McDiarmid , Nikola Yolov

Fomin, Kratochv\'il, Lokshtanov, Mancini, and Telle showed that every $C_{4}$-free graph is reconstructible from the \emph{multiset} of closed neighborhoods. We strengthen their result proving that every $C_{4}$-free graph is…

Combinatorics · Mathematics 2025-10-27 Steffen Borgwardt , MacKenzie Carr , Ce Chen , Wayne Ge , Stephen G. Hartke , Yixuan Huang , Alex Moon

We call an $\alpha \in \mathbb{R}$ regainingly approximable if there exists a computable nondecreasing sequence $(a_n)_n$ of rational numbers converging to $\alpha$ with $\alpha - a_n < 2^{-n}$ for infinitely many $n \in \mathbb{N}$. We…

Logic · Mathematics 2026-02-11 Peter Hertling , Rupert Hölzl , Philip Janicki

Random geometric graphs (RGGs) are commonly used to model networked systems that depend on the underlying spatial embedding. We concern ourselves with the probability distribution of an RGG, which is crucial for studying its random…

Information Theory · Computer Science 2018-01-16 Mihai-Alin Badiu , Justin P. Coon

We connect two seemingly unrelated problems in graph theory. Any graph $G$ has an associated neighborhood multiset $\mathscr{N}(G)= \{N(x) \mid x\in V(G)\}$ whose elements are precisely the open vertex-neighborhoods of $G$. In general there…

Combinatorics · Mathematics 2019-11-14 Richard H. Hammack , Cristina Mullican

In this work we perform a detailed statistical analysis of topological and spectral properties of random geometric graphs (RGGs); a graph model used to study the structure and dynamics of complex systems embedded in a two dimensional space.…

Disordered Systems and Neural Networks · Physics 2020-10-21 R. Aguilar-Sanchez , J. A. Mendez-Bermudez , Francisco A. Rodrigues , Jose M. Sigarreta

We study the algorithmic decidability of the domination number in the Erdos-Renyi random graph model $G(n,p)$. We show that for a carefully chosen edge probability $p=p(n)$, the domination problem exhibits a strong irreducible property.…

Computational Complexity · Computer Science 2026-04-28 Guangyan Zhou

A recent result of Condon, Kim, K\"{u}hn and Osthus implies that for any $r\geq (\frac{1}{2}+o(1))n$, an $n$-vertex almost $r$-regular graph $G$ has an approximate decomposition into any collections of $n$-vertex bounded degree trees. In…

Combinatorics · Mathematics 2018-08-28 Jaehoon Kim , Younjin Kim , Hong Liu

A graph is $\ell$-reconstructible if it is determined by its multiset of induced subgraphs obtained by deleting $\ell$ vertices. We prove that strongly regular graphs with at least six vertices are $2$-reconstructible.

Combinatorics · Mathematics 2022-10-24 Douglas B. West , Xuding Zhu

In the anisotropic random geometric graph model, vertices correspond to points drawn from a high-dimensional Gaussian distribution and two vertices are connected if their distance is smaller than a specified threshold. We study when it is…

Statistics Theory · Mathematics 2022-07-01 Matthew Brennan , Guy Bresler , Brice Huang

In the random geometric graph model $\mathsf{Geo}_d(n,p)$, we identify each of our $n$ vertices with an independently and uniformly sampled vector from the $d$-dimensional unit sphere, and we connect pairs of vertices whose vectors are…

Probability · Mathematics 2021-11-23 Siqi Liu , Sidhanth Mohanty , Tselil Schramm , Elizabeth Yang

Two graphs $G$ and $H$ are \emph{hypomorphic} if there exists a bijection $\varphi \colon V(G) \rightarrow V(H)$ such that $G - v \cong H - \varphi(v)$ for each $v \in V(G)$. A graph $G$ is \emph{reconstructible} if $H \cong G$ for all $H$…

Combinatorics · Mathematics 2018-01-23 Nathan Bowler , Joshua Erde , Peter Heinig , Florian Lehner , Max Pitz

Consider a $d$-uniform random hypergraph on $n$ vertices in which hyperedges are included iid so that the average degree is $n^\delta$. The projection of a hypergraph is a graph on the same $n$ vertices where an edge connects two vertices…

Combinatorics · Mathematics 2025-02-24 Guy Bresler , Chenghao Guo , Yury Polyanskiy , Andrew Yao

This work addresses a modification of the random geometric graph (RGG) model by considering a set of points uniformly and independently distributed on the surface of a $(d-1)$-sphere with radius $r$ in a $d-$dimensional Euclidean space,…

Physics and Society · Physics 2018-10-03 Alfonso Allen-Perkins

The graph reconstruction conjecture asserts that every finite simple graph on at least three vertices can be reconstructed up to isomorphism from its deck - the collection of its vertex-deleted subgraphs. Kocay's Lemma is an important tool…

Combinatorics · Mathematics 2014-09-09 Igor C. Oliveira , Bhalchandra D. Thatte

In the planted partition problem, the $n$ vertices of a random graph are partitioned into $k$ "clusters," and edges between vertices in the same cluster and different clusters are included with constant probability $p$ and $q$, respectively…

Data Structures and Algorithms · Computer Science 2017-08-24 Sam Cole

Hypergraphs are structures that can be decomposed or described; in other words they are recursively countable. Here, we get exact and asymptotic enumeration results on hypergraphs by means of exponential generating functions. The number of…

Discrete Mathematics · Computer Science 2008-06-20 Tsiriniaina Andriamampianina

Many machine learning algorithms used for dimensional reduction and manifold learning leverage on the computation of the nearest neighbours to each point of a dataset to perform their tasks. These proximity relations define a so-called…

Statistical Mechanics · Physics 2020-07-22 Vittorio Erba , Sebastiano Ariosto , Marco Gherardi , Pietro Rotondo