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Graph embedding, representing local and global neighborhood information by numerical vectors, is a crucial part of the mathematical modeling of a wide range of real-world systems. Among the embedding algorithms, random walk-based algorithms…

Social and Information Networks · Computer Science 2022-07-06 Sarmad N. Mohammed , Semra Gündüç

We initiate the study of property testing in arbitrary planar graphs. We prove that bipartiteness can be tested in constant time, improving on the previous bound of $\tilde{O}(\sqrt{n})$ for graphs on $n$ vertices. The constant-time…

Data Structures and Algorithms · Computer Science 2018-12-27 Artur Czumaj , Morteza Monemizadeh , Krzysztof Onak , Christian Sohler

Suppose in a graph $G$ vertices can be either red or blue. Let $k$ be odd. At each time step, each vertex $v$ in $G$ polls $k$ random neighbours and takes the majority colour. If it doesn't have $k$ neighbours, it simply polls all of them,…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-08-01 Mohammed Amin Abdullah , Moez Draief

Let $G$ be a connected semisimple real Lie group with finite center, and $\mu$ a probability measure on $G$ whose support generates a Zariski-dense subgroup of $G$. We consider the right $\mu$-random walk on $G$ and show that each random…

Dynamical Systems · Mathematics 2022-10-18 Timothée Bénard

We study lower bounds for the problem of approximating a one dimensional distribution given (noisy) measurements of its moments. We show that there are distributions on $[-1,1]$ that cannot be approximated to accuracy $\epsilon$ in…

Data Structures and Algorithms · Computer Science 2023-07-04 Yujia Jin , Christopher Musco , Aaron Sidford , Apoorv Vikram Singh

Consider $G=SL_2(\mathbb{Z})/\{\pm I\}$ acting on the complex upper half plane $H$ by $h_M(z)=\frac{az+b}{cz+d},$ for $M \in G$. Let $D=\{z \in H: |z|\geq 1, |\Re(z)|\leq 1/2\}$. We consider the set $\mathcal{E} \subset G$ with the $9$…

Probability · Mathematics 2017-08-09 Gerard Letac , Mauro Piccioni

Random walk based sampling methods have been widely used in graph sampling in recent years, while it has bias towards higher degree nodes in the sample. To overcome this deficiency, classical methods such as GMD modify the topology of…

Methodology · Statistics 2022-09-27 Xiao Qi

Random walks on bounded degree expander graphs have numerous applications, both in theoretical and practical computational problems. A key property of these walks is that they converge rapidly to their stationary distribution. In this work…

Computational Complexity · Computer Science 2016-09-15 Tali Kaufman , David Mass

Graph vertex embeddings based on random walks have become increasingly influential in recent years, showing good performance in several tasks as they efficiently transform a graph into a more computationally digestible format while…

Machine Learning · Statistics 2021-07-22 Dominik Kloepfer , Angelica I. Aviles-Rivero , Daniel Heydecker

We propose a model of random walks on weighted graphs where the weights are interval valued, and connect it to reversible imprecise Markov chains. While the theory of imprecise Markov chains is now well established, this is a first attempt…

Optimization and Control · Mathematics 2016-09-20 Damjan Škulj

For a vertex $v$, let $c_G(v)$ be the order of the largest clique containing $v$, and let $w_r(v)$ be the number of walks with $r$ vertices starting at $v$. We prove that, for every finite simple graph $G$ and every integer $r\ge 1$,…

Combinatorics · Mathematics 2026-05-05 Feng Liu , Shuang Sun , Yan Wang , Qi Wu

We investigate the use of discrete-time quantum walks to sample from an almost-uniform distribution, in the absence of any external source of randomness. Integers are encoded on the vertices of a cycle graph, and a quantum walker evolves…

Quantum Physics · Physics 2025-11-12 Marco Radaelli , Claudia Benedetti , Stefano Olivares

Let $d\geq 3$ be fixed and $G$ be a large random $d$-regular graph on $n$ vertices. We show that if $n$ is large enough then the entry distribution of every almost eigenvector $v$ of $G$ (with entry sum 0 and normalized to have length…

Probability · Mathematics 2016-07-19 Agnes Backhausz , Balazs Szegedy

Random walk on changing graphs is considered. For sequences of finite graphs increasing monotonically towards a limiting infinite graph, we establish transition probability upper bounds. It yields sufficient transience criteria for simple…

Probability · Mathematics 2018-10-09 Ruojun Huang

The random walk with choice is a well known variation to the random walk that first selects a subset of $d$ neighbours nodes and then decides to move to the node which maximizes the value of a certain metric; this metric captures the number…

Data Structures and Algorithms · Computer Science 2010-07-20 John Alexandris , Gregory Karagiorgos 'and' Ioannis Stavrakakis

Let $\mu$ be a borelian probability measure on $\mathbf{G}:=\mathrm{SL}_d(\mathbb{Z}) \ltimes \mathbb{T}^d$. Define, for $x\in \mathbb{T}^d$, a random walk starting at $x$ denoting for $n\in \mathbb{N}$, \[ \left\{\begin{array}{rcl} X_0…

Probability · Mathematics 2017-02-28 Jean-baptiste Boyer

We consider random walk on a finite group $G$ as follows. We can consider $G$ as a group of substitutions. Randomly (i.e. with probability $U(g)=|G|^{-1}$ ) we choose a substitution $g \in G$ and execute it twice in a row, i.e. execute a…

Representation Theory · Mathematics 2023-07-11 Olexandr Vyshnevetskiy , Alexander Bendikov

We present a novel quasi-Monte Carlo mechanism to improve graph-based sampling, coined repelling random walks. By inducing correlations between the trajectories of an interacting ensemble such that their marginal transition probabilities…

Machine Learning · Statistics 2024-05-27 Isaac Reid , Eli Berger , Krzysztof Choromanski , Adrian Weller

We give a deterministic, nearly logarithmic-space algorithm that given an undirected graph $G$, a positive integer $r$, and a set $S$ of vertices, approximates the conductance of $S$ in the $r$-step random walk on $G$ to within a factor of…

Computational Complexity · Computer Science 2019-11-26 Jack Murtagh , Omer Reingold , Aaron Sidford , Salil Vadhan

We present several refinements on the fluctuations of sequences of random vectors (with values in the Euclidean space $\mathbb{R}^d$) which converge after normalization to a multidimensional Gaussian distribution. More precisely we refine…

Probability · Mathematics 2022-03-04 Pierre-Loïc Méliot , Ashkan Nikeghbali