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We show that for any fixed dense graph G and bounded-degree tree T on the same number of vertices, a modest random perturbation of G will typically contain a copy of T . This combines the viewpoints of the well-studied problems of embedding…

Combinatorics · Mathematics 2025-05-30 Michael Krivelevich , Matthew Kwan , Benny Sudakov

We study "positive" graphs that have a nonnegative homomorphism number into every edge-weighted graph (where the edgeweights may be negative). We conjecture that all positive graphs can be obtained by taking two copies of an arbitrary…

Combinatorics · Mathematics 2014-01-31 Omar Antolín Camarena , Endre Csóka , Tamás Hubai , Gábor Lippner , László Lovász

The strong geodetic problem is to find the smallest number of vertices such that by fixing one shortest path between each pair, all vertices of the graph are covered. In this paper we study the strong geodetic problem on complete bipartite…

Combinatorics · Mathematics 2018-06-04 Vesna Iršič , Matjaž Konvalinka

Given a graph $G$, its Ramsey number $r(G)$ is the minimum $N$ so that every two-coloring of $E(K_N)$ contains a monochromatic copy of $G$. It was conjectured by Conlon, Fox, and Sudakov that if one deletes a single vertex from $G$, the…

Combinatorics · Mathematics 2024-01-17 Yuval Wigderson

For a graph $H$ and an integer $n$, we let $nH$ denote the disjoint union of $n$ copies of $H$. In 1975, Burr, Erd\H{o}s, and Spencer initiated the study of Ramsey numbers for $nH$, one of few instances for which Ramsey numbers are now…

Combinatorics · Mathematics 2022-12-06 Aurelio Sulser , Miloš Trujić

The theory of convergent graph sequences has been worked out in two extreme cases, dense graphs and bounded degree graphs. One can define convergence in terms of counting homomorphisms from fixed graphs into members of the sequence…

Combinatorics · Mathematics 2010-02-02 Christian Borgs , Jennifer Chayes , Jeff Kahn , László Lovász

I report on the development of a novel statistical mechanical formalism for the analysis of random graphs with many short loops, and processes on such graphs. The graphs are defined via maximum entropy ensembles, in which both the degrees…

Disordered Systems and Neural Networks · Physics 2016-05-04 A C C Coolen

We consider three extremal problems about the number of copies of a fixed graph in another larger graph. First, we correct an error in a result of Reiher and Wagner and prove that the number of $k$-edge stars in a graph with density $x \in…

Combinatorics · Mathematics 2024-03-19 Emily Cairncross , Dhruv Mubayi

We introduce Tiered Sampling, a novel technique for approximate counting sparse motifs in massive graphs whose edges are observed in a stream. Our technique requires only a single pass on the data and uses a memory of fixed size $M$, which…

Data Structures and Algorithms · Computer Science 2017-10-06 Lorenzo De Stefani , Erisa Terolli , Eli Upfal

In this paper, motivated by network inference and tomography applications, we study the problem of compressive sensing for sparse signal vectors over graphs. In particular, we are interested in recovering sparse vectors representing the…

Information Theory · Computer Science 2010-08-06 Weiyu Xu , Enrique Mallada , Ao Tang

Network (or graph) sparsification compresses a graph by removing inessential edges. By reducing the data volume, it accelerates or even facilitates many downstream analyses. Still, the accuracy of many sparsification methods, with…

Social and Information Networks · Computer Science 2023-09-28 Zhen Su , Jürgen Kurths , Henning Meyerhenke

Given a connected graph $G$ on $n$ vertices and a positive integer $k\le n$, a subgraph of $G$ on $k$ vertices is called a $k$-subgraph in $G$. We design combinatorial approximation algorithms for finding a connected $k$-subgraph in $G$…

Discrete Mathematics · Computer Science 2015-01-30 Xujin Chen , Xiaodong Hu , Changjun Wang

This paper looks at the task of network topology inference, where the goal is to learn an unknown graph from nodal observations. One of the novelties of the approach put forth is the consideration of prior information about the density of…

Signal Processing · Electrical Eng. & Systems 2022-07-12 Samuel Rey , T. Mitchell Roddenberry , Santiago Segarra , Antonio G. Marques

Graph decompositions are the natural generalisation of tree decompositions where the decomposition tree is replaced by a genuine graph. Recently they found theoretical applications in the theory of sparsity, topological graph theory,…

Discrete Mathematics · Computer Science 2023-12-20 Johannes Carmesin , Sarah Frenkel

Bipartite networks manifest as a stream of edges that represent transactions, e.g., purchases by retail customers. Many machine learning applications employ neighborhood-based measures to characterize the similarity among the nodes, such as…

Social and Information Networks · Computer Science 2018-05-09 Nesreen K. Ahmed , Nick Duffield , Liangzhen Xia

High triangle density -- the graph property stating that a constant fraction of two-hop paths belong to a triangle -- is a common signature of social networks. This paper studies triangle-dense graphs from a structural perspective. We prove…

Data Structures and Algorithms · Computer Science 2014-02-10 Rishi Gupta , Tim Roughgarden , C. Seshadhri

Two-sided popular matchings in bipartite graphs are a well-known generalization of stable matchings in the marriage setting, and they are especially relevant when preference lists are incomplete. In this case, the cardinality of a stable…

Discrete Mathematics · Computer Science 2018-03-13 Yuri Faenza , Vladlena Powers , Xingyu Zhang

We design an efficient sampling algorithm to generate samples from the hardcore model on random regular bipartite graphs as long as $\lambda \lesssim \frac{1}{\sqrt{\Delta}}$, where $\Delta$ is the degree. Combined with recent work of…

Data Structures and Algorithms · Computer Science 2026-04-24 Nicholas Kocurek , Shayan Oveis Gharan , Dante Tjowasi

We solve the graph bi-partitioning problem in dense graphs with arbitrary degree distribution using the replica method. We find the cut-size to scale universally with <k^1/2>. In contrast, earlier results studying the problem in graphs with…

Disordered Systems and Neural Networks · Physics 2008-12-11 Joerg Reichardt , Stefan Bornholdt

Graph signals are functions of the underlying graph. When the edge-weight between a pair of nodes is high, the corresponding signals generally have a higher correlation. As a result, the signals can be represented in terms of a graph-based…

Signal Processing · Electrical Eng. & Systems 2024-09-09 Rishabh Ravi , Kaushani Majumder , Kalp Vyas , Satish Mulleti
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