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A deck of $n$ cards are shuffled by repeatedly taking off the top card, flipping it with probability $1/2$, and inserting it back into the deck at a random position. This process can be considered as a Markov chain on the group $B_n$ of…

Combinatorics · Mathematics 2023-03-15 Fumihiko Nakano , Taizo Sadahiro , Tetsuya Sakurai

We study the problem of generating a sample from the stationary distribution of a Markov chain, given a method to simulate the chain. We give an approximation algorithm for the case of a random walk on a regular graph with n vertices that…

Probability · Mathematics 2007-05-23 Itai Benjamini , Ben Morris

We evaluate the mixing time of certain random walks on large unitary groups

Probability · Mathematics 2015-05-18 Jean Bourgain

We develop a method for analyzing the mixing times for a quite general class of Markov chains on the complete monomial group G \wr S_n (the wreath product of a group G with the permutation group S_n) and a quite general class of Markov…

Probability · Mathematics 2012-08-27 James Allen Fill , Clyde H. Schoolfield,

We study the rate of convergence to equilibrium of the self-repellent random walk and its local time process on the discrete circle $\mathbb{Z}_n$. While the self-repellent random walk alone is non-Markovian since the jump rates depend on…

Probability · Mathematics 2025-12-01 Andreas Eberle , Francis Lörler

This paper introduces the Attracting Random Walks model, which describes the dynamics of a system of particles on a graph with $n$ vertices. At each step, a single particle moves to an adjacent vertex (or stays at the current one) with…

Probability · Mathematics 2020-06-01 Julia Gaudio , Yury Polyanskiy

We study the mixing time of the Dikin walk in a polytope - a random walk based on the log-barrier from the interior point method literature. This walk, and a close variant, were studied by Narayanan (2016) and Kannan-Narayanan (2012).…

Data Structures and Algorithms · Computer Science 2016-08-10 Sushant Sachdeva , Nisheeth K. Vishnoi

We propose a new approach for estimating the finite dimensional transition matrix of a Markov chain using a large number of independent sample paths observed at random times. The sample paths may be observed as few as two times, and the…

Methodology · Statistics 2025-05-20 Daphne Aurouet , Valentin Patilea

We study random walks on the giant component of the Erd\H{o}s-R\'enyi random graph ${\cal G}(n,p)$ where $p=\lambda/n$ for $\lambda>1$ fixed. The mixing time from a worst starting point was shown by Fountoulakis and Reed, and independently…

Probability · Mathematics 2016-10-21 Nathanael Berestycki , Eyal Lubetzky , Yuval Peres , Allan Sly

Since 1997 a considerable effort has been spent to study the mixing time of switch Markov chains on the realizations of graphic degree sequences of simple graphs. Several results were proved on rapidly mixing Markov chains on unconstrained,…

We investigate the hitting times of random walks on graphs, where a hitting time is defined as the number of steps required for a random walker to move from one node to another. While much of the existing literature focuses on calculating…

Probability · Mathematics 2025-11-10 Anuraag Kumar

We suggest a non-asymptotic matrix perturbation-theoretic approach to get sharp bounds on the expected meeting time of random walks on large (possibly random) graphs. We provide a formula for the expected meeting time in terms of the…

Probability · Mathematics 2024-07-09 Thomas van Belle , Anton Klimovsky

The paper deals with fluctuations of Kendall random walks, which are extremal Markov chains and iterated integral transforms with the Williamson kernel $\Psi(t) = \left(1-|t|^{\alpha}\right)_+$, $\alpha>0$. We obtain the joint distribution…

Probability · Mathematics 2022-05-19 Barbara Jasiulis-Gołdyn , Edward Omey , Mateusz Staniak

Quantum walk search may exhibit phenomena beyond the intuition from a conventional random walk theory. One of such examples is exceptional configuration phenomenon -- it appears that it may be much harder to find any of two or more marked…

Quantum Physics · Physics 2021-01-13 Adam Glos , Nikolajs Nahimovs , Konstantin Balakirev , Kamil Khadiev

We introduce weighted Markovian graphs, a random walk model that decouples the transition dynamics of a Markov chain from (random) edge weights representing the cost of traversing each edge. This decoupling allows us to study the…

Optimization and Control · Mathematics 2026-03-30 Thao Le , Robbert van der Burg , Bernd Heidergott , Ines Lindner , Alessandro Zocca

Higher order random walks (HD-walks) on high dimensional expanders (HDX) have seen an incredible amount of study and application since their introduction by Kaufman and Mass [KM16], yet their broader combinatorial and spectral properties…

Computational Complexity · Computer Science 2021-07-20 Mitali Bafna , Max Hopkins , Tali Kaufman , Shachar Lovett

We analyze continuous-time quantum walks on necklace graphs - cyclical graphs consisting of many copies of a smaller graph (pearl). Using a Bloch-type ansatz for the eigenfunctions, we block-diagonalize the Hamiltonian, reducing the…

Quantum Physics · Physics 2012-04-16 Maria Kieferova , Daniel Nagaj

Message-passing architectures struggle to sufficiently model long-range dependencies in node and graph prediction tasks. We propose a novel approach exploiting hierarchical graph structures and adaptive random walks to address this…

Machine Learning · Computer Science 2025-09-03 Joël Mathys , Federico Errica

Despite of the extreme success of the spectral graph theory, there are relatively few papers applying spectral analysis to hypergraphs. Chung first introduced Laplacians for regular hypergraphs and showed some useful applications. Other…

Combinatorics · Mathematics 2011-02-23 Linyuan Lu , Xing Peng

We develop a general theory for Markov chains whose transition probabilities are the coefficients of descent operators on combinatorial Hopf algebras. These model the breaking-then-recombining of combinational objects. Examples include the…

Combinatorics · Mathematics 2018-08-28 C. Y. Amy Pang