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Introduced in 1963, Glauber dynamics is one of the most practiced and extensively studied methods for sampling the Ising model on lattices. It is well known that at high temperatures, the time it takes this chain to mix in $L^1$ on a system…

Probability · Mathematics 2015-05-14 Eyal Lubetzky , Allan Sly

We present an algorithm for updating the Reeb graph under fully dynamic changes of the function values. The basic event is the interchange of two consecutive vertex values. The algorithm updates the Reeb graph in $O(l g{n})$ worst-case…

Computational Geometry · Computer Science 2015-07-20 Salman Parsa

We resolve a number of long-standing open problems in online graph coloring. More specifically, we develop tight lower bounds on the performance of online algorithms for fundamental graph classes. An important contribution is that our…

Data Structures and Algorithms · Computer Science 2017-07-04 Susanne Albers , Sebastian Schraink

We address the problem of sampling colorings of a graph $G$ by Markov chain simulation. For most of the article we restrict attention to proper $q$-colorings of a path on $n$ vertices (in statistical physics terms, the one-dimensional…

Probability · Mathematics 2007-05-23 Martin Dyer , Leslie Ann Goldberg , Mark Jerrum

The mixing time of a graph is an important metric, which is not only useful in analyzing connectivity and expansion properties of the network, but also serves as a key parameter in designing efficient algorithms. We introduce a new notion…

Distributed, Parallel, and Cluster Computing · Computer Science 2018-01-09 Anisur Rahaman Molla , Gopal Pandurangan

We consider a dynamic random graph on $n$ vertices that is obtained by starting from a random graph generated according to the configuration model with a prescribed degree sequence and at each unit of time randomly rewiring a fraction…

Probability · Mathematics 2018-03-14 Luca Avena , Hakan Guldas , Remco van der Hofstad , Frank den Hollander

This paper considers non-backtracking random walks on random graphs generated according to the configuration model. The quantity of interest is the scaling of the mixing time of the random walk as the number of vertices of the random graph…

Probability · Mathematics 2022-09-15 Luca Avena , Hakan Güldaş , Remco van der Hofstad , Frank den Hollander , Oliver Nagy

In this note we confirm a conjecture raised by Benjamini et al. \cite{BST} on the acquaintance time of graphs, proving that for all graphs $G$ with $n$ vertices it holds that $\AC(G) = O(n^{3/2})$, which is tight up to a multiplicative…

Combinatorics · Mathematics 2013-07-24 Omer Angel , Igor Shinkar

Mahlmann and Schindelhauer (2005) defined a Markov chain which they called $k$-Flipper, and showed that it is irreducible on the set of all connected regular graphs of a given degree (at least 3). We study the 1-Flipper chain, which we call…

Discrete Mathematics · Computer Science 2018-06-14 Colin Cooper , Martin Dyer , Catherine Greenhill , Andrew Handley

In this paper, we provide upper and lower bounds on the crossing numbers of dense graphs on surfaces, which match up to constant factors. First, we prove that if $G$ is a dense enough graph with $m$ edges and $\Sigma$ is a surface of genus…

Combinatorics · Mathematics 2025-06-12 Alfredo Hubard , Arnaud de Mesmay , Hugo Parlier

Given a finite graph G, a vertex of the lamplighter graph consists of a zero-one labeling of the vertices of G, and a marked vertex of G. For transitive graphs G, we show that, up to constants, the relaxation time for simple random walk in…

Probability · Mathematics 2007-05-23 Yuval Peres , David Revelle

In this paper we establish a diffusion limit for a multivariate continuous time Markov chain whose components are indexed by vertices of a finite graph. The components take values in a common finite set of non-negative integers and evolve…

Probability · Mathematics 2025-09-15 Anatolii Puhalskii , Vadim Shcherbakov

In their seminal paper from 2004, Kuhn, Moscibroda, and Wattenhofer (KMW) proved a hardness result for several fundamental graph problems in the LOCAL model: For any (randomized) algorithm, there are input graphs with $n$ nodes and maximum…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-05-10 Corinna Coupette , Christoph Lenzen

The Swendsen-Wang dynamics is a popular algorithm for sampling from the Gibbs distribution for the ferromagnetic Ising model on a graph $G=(V,E)$. The dynamics is a "global" Markov chain which is conjectured to converge to equilibrium in…

Discrete Mathematics · Computer Science 2018-06-13 Antonio Blanca , Zongchen Chen , Eric Vigoda

The "slope-number" of a graph $G$ is the minimum number of distinct edge slopes in a straight-line drawing of $G$ in the plane. We prove that for $\Delta\geq5$ and all large $n$, there is a $\Delta$-regular $n$-vertex graph with…

Combinatorics · Mathematics 2008-09-09 Vida Dujmovic' , Matthew Suderman , David R. Wood

We show an $\Omega\big(\Delta^{\frac{1}{3}-\frac{\eta}{3}}\big)$ lower bound on the runtime of any deterministic distributed $\mathcal{O}\big(\Delta^{1+\eta}\big)$-graph coloring algorithm in a weak variant of the \LOCAL\ model. In…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-09-15 Dan Hefetz , Fabian Kuhn , Yannic Maus , Angelika Steger

We consider the following dynamic load-balancing process: given an underlying graph $G$ with $n$ nodes, in each step $t\geq 0$, one unit of load is created, and placed at a randomly chosen graph node. In the same step, the chosen node picks…

Data Structures and Algorithms · Computer Science 2020-03-23 Dan Alistarh , Giorgi Nadiradze , Amirmojtaba Sabour

In the Fastest Mixing Markov Chain problem, we are given a graph $G = (V, E)$ and desire the discrete-time Markov chain with smallest mixing time $\tau$ subject to having equilibrium distribution uniform on $V$ and non-zero transition…

Probability · Mathematics 2024-12-11 Sam Olesker-Taylor , Luca Zanetti

Gaussian graphical model selection is usually studied under independent sampling, but in many applications observations arise from dependent dynamics. We study structure learning when the data consist of a single trajectory of Gaussian…

Machine Learning · Computer Science 2026-05-13 Vignesh Tirukkonda , Anirudh Rayas , Gautam Dasarathy

We prove a cutoff for the random walk on random $n$-lifts of finite weighted graphs, even when the random walk on the base graph $\mathcal{G}$ of the lift is not reversible. The mixing time is w.h.p. $t_{mix}=h^{-1}\log n$, where $h$ is a…

Probability · Mathematics 2019-08-09 Guillaume Conchon--Kerjan