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A new class of distances for graph vertices is proposed. This class contains parametric families of distances which reduce to the shortest-path, weighted shortest-path, and the resistance distances at the limiting values of the family…

Combinatorics · Mathematics 2011-01-25 Pavel Chebotarev

Let $(M,g_1)$ be a complete $d$-dimensional Riemannian manifold for $d > 1$. Let $\mathcal X_n$ be a set of $n$ sample points in $M$ drawn randomly from a smooth Lebesgue density $f$ supported in $M$. Let $x,y$ be two points in $M$. We…

Probability · Mathematics 2016-11-07 Sung Jin Hwang , Steven B. Damelin , Alfred O. Hero

Given a Riemannian manifold $(M,g)$ and a geodesic $\gamma$, the perpendicular part of the derivative of the geodesic flow $\phi_g^t: SM \rightarrow SM$ along $\gamma$ is a linear symplectic map. We give an elementary proof of the following…

Dynamical Systems · Mathematics 2013-12-04 Daniel Visscher

We consider geodesics for first passage percolation (FPP) on $\mathbb{Z}^d$ with iid passage times. As has been common in the literature, we assume that the FPP system satisfies certain basic properties conjectured to be true, and derive…

Probability · Mathematics 2022-05-04 Kenneth S. Alexander

Let $G_{n,p}$ be the standard Erd\H{o}s-R\'enyi-Gilbert random graph and let $G_{n,n,p}$ be the random bipartite graph on $n+n$ vertices, where each $e\in [n]^2$ appears as an edge independently with probability $p$. For a graph $G=(V,E)$,…

Combinatorics · Mathematics 2015-11-19 Alan Frieze , Tony Johansson

A methodology is developed for data analysis based on empirically constructed geodesic metric spaces. For a probability distribution, the length along a path between two points can be defined as the amount of probability mass accumulated…

Statistics Theory · Mathematics 2019-03-18 Kei Kobayashi , Henry P. Wynn

We describe a method for approximating a single-variable function $f$ using persistence diagrams of sublevel sets of $f$ from height functions in different directions. We provide algorithms for the piecewise linear case and for the smooth…

Algebraic Topology · Mathematics 2023-02-10 Aina Ferrà , Carles Casacuberta , Oriol Pujol

A geodesic is a shortest path which connects a pair of vertices of a graph G. In this paper we define the geodesic subpath number gpn(G) of a graph G as the number of geodesics in G. The number of subtrees and subpaths are already studied…

Combinatorics · Mathematics 2026-04-07 Martin Knor , Jelena Sedlar , Riste Škrekovski , Xiao-Dong Zhang

Let $(\Sigma, g)$ be a closed, oriented, negatively curved surface, and fix pairwise disjoint simple closed geodesics $\gamma_{\star,1}, \dots \gamma_{\star, r}$. We give an asymptotic growth as $L \to +\infty$ of the number of primitive…

Dynamical Systems · Mathematics 2024-03-20 Yann Chaubet

A maximal geodesic in a graph is a geodesic (alias shortest path) which is not a subpath of a longer geodesic. The geodesic-transversal problem in a graph $G$ is introduced as the task to find a smallest set $S$ of vertices of $G$ such that…

Combinatorics · Mathematics 2021-01-21 Paul Manuel , Boštjan Brešar , Sandi Klavžar

In the original (1961) Gilbert model of random geometric graphs, nodes are placed according to a Poisson point process, and links formed between those within a fixed range. Motivated by wireless ad-hoc networks "soft" or "probabilistic"…

Statistical Mechanics · Physics 2017-06-15 Carl P. Dettmann , Orestis Georgiou

For an integer $s\geq1$ and a graph $\Gamma$, a path $(u_0, u_1, \ldots, u_{s})$ composed of vertices of $\Gamma$ is called an {\em $s$-geodesic} if it is a shortest path between $u_0$ and $u_s$. We say that $\Gamma$ is {\em $s$-geodesic…

Combinatorics · Mathematics 2025-12-29 Jun-Jie Huang

Consider a weighted or unweighted k-nearest neighbor graph that has been built on n data points drawn randomly according to some density p on R^d. We study the convergence of the shortest path distance in such graphs as the sample size…

Machine Learning · Computer Science 2012-07-10 Morteza Alamgir , Ulrike von Luxburg

Recall that Janson showed that if the edges of the complete graph $K_n$ are assigned exponentially distributed independent random weights, then the expected length of a shortest path between a fixed pair of vertices is asymptotically equal…

Combinatorics · Mathematics 2021-06-01 Alan Frieze , Wesley Pegden , Gregory Sorkin , Tomasz Tkocz

In this work we study the time complexity for the search of local minima in random graphs whose vertices have i.i.d. cost values. We show that, for Erd\"os-R\'enyi graphs with connection probability given by $\lambda/n^\alpha$ (with…

Data Structures and Algorithms · Computer Science 2021-05-11 Matthieu Jonckheere , Manuel Sáenz

In first-passage percolation (FPP), one places nonnegative random variables (weights) $(t_e)$ on the edges of a graph and studies the induced weighted graph metric. We consider FPP on $\mathbb{Z}^d$ for $d \geq 2$ and analyze the geometric…

Probability · Mathematics 2020-03-09 Gerandy Brito , Michael Damron , Jack Hanson

We consider planar directed last-passage percolation on the square lattice with general i.i.d. weights and study the geometry of the full set of semi-infinite geodesics in a typical realization of the random environment. The structure of…

Probability · Mathematics 2023-08-01 Christopher Janjigian , Firas Rassoul-Agha , Timo Seppäläinen

A standard way to approximate the distance between any two vertices $p$ and $q$ on a mesh is to compute, in the associated graph, a shortest path from $p$ to $q$ that goes through one of $k$ sources, which are well-chosen vertices.…

Computational Geometry · Computer Science 2016-08-29 Pegah Kamousi , Sylvain Lazard , Anil Maheshwari , Stefanie Wuhrer

Random geometric graphs are random graph models defined on metric spaces. Such a model is defined by first sampling points from a metric space and then connecting each pair of sampled points with probability that depends on their distance,…

Machine Learning · Computer Science 2026-04-10 Han Huang , Pakawut Jiradilok , Elchanan Mossel

The importance of aggregated count data, which is calculated from the data of multiple individuals, continues to increase. Collective Graphical Model (CGM) is a probabilistic approach to the analysis of aggregated data. One of the most…

Machine Learning · Statistics 2021-02-19 Yasunori Akagi , Naoki Marumo , Hideaki Kim , Takeshi Kurashima , Hiroyuki Toda