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Consider a system of $K$ particles moving on the vertex set of a finite connected graph with at most one particle per vertex. If there is one, the particle at $x$ chooses one of the $\hbox{deg} (x)$ neighbors of its location uniformly at…

Probability · Mathematics 2019-06-06 Shiba Biswal , Nicolas Lanchier

Matrix-based centrality measures have enjoyed significant popularity in network analysis, in no small part due to our ability to rigorously analyze their behavior as parameters vary. Recent work has considered the relationship between…

Social and Information Networks · Computer Science 2019-02-06 Eric Horton , Kyle Kloster , Blair D. Sullivan

We study the variable-length ensemble of self-avoiding walks on the complete graph. We obtain the leading order asymptotics of the mean and variance of the walk length, as the number of vertices goes to infinity. Central limit theorems for…

Mathematical Physics · Physics 2019-11-26 Youjin Deng , Timothy M Garoni , Jens Grimm , Abrahim Nasrawi , Zongzheng Zhou

A random graph evolution based on the interactions of N vertices is studied. During the evolution both the preferential attachment method and the uniform choice of vertices are allowed. The weight of a vertex means the number of its…

Probability · Mathematics 2014-05-07 István Fazekas , Bettina Porvázsnyik

We consider the set of all graphs on n labeled vertices with prescribed degrees D=(d_1, ..., d_n). For a wide class of tame degree sequences D we prove a computationally efficient asymptotic formula approximating the number of graphs within…

Combinatorics · Mathematics 2011-12-05 Alexander Barvinok , J. A. Hartigan

We give new general formulas for the asymptotics of the number of spanning trees of a large graph. A special case answers a question of McKay (1983) for regular graphs. The general answer involves a quantity for infinite graphs that we call…

Combinatorics · Mathematics 2010-04-27 Russell Lyons

Graph-limit theory focuses on the convergence of sequences of graphs when the number of nodes becomes arbitrarily large. This framework defines a continuous version of graphs allowing for the study of dynamical systems on very large graphs,…

Probability · Mathematics 2020-05-20 Julien Petit , Renaud Lambiotte , Timoteo Carletti

It is well known that if a network topology is a path or line and the states of vertices or nodes evolve according to the consensus policy, then the network is Laplacian controllable by an input connected to its terminal vertex. In this…

Optimization and Control · Mathematics 2019-02-18 Shun-Pin Hsu , Ping-Yen Yang

In this paper, the leading term of the asymptotics of the number of possible final positions of a random walk on a directed Hamiltonian metric graph is found. Consideration of such dynamical systems could be motivated by problems of…

Mathematical Physics · Physics 2021-12-28 Daniil Pyatko , Vsevolod Chernyshev

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

We study the asymptotics of large, simple, labeled graphs constrained by the densities of edges and of $k$-star subgraphs, $k\ge 2$ fixed. We prove that under such constraints graphs are "multipodal": asymptotically in the number of…

Combinatorics · Mathematics 2017-03-16 Richard Kenyon , Charles Radin , Kui Ren , Lorenzo Sadun

We introduce a graph-signal generalisation of Sample Entropy, denoted SampEn$_{G}$, to quantify irregularity of graph signals on a continuous state space, complementing existing methods on symbolic dynamics. Our approach replaces the…

Signal Processing · Electrical Eng. & Systems 2026-04-23 Mei-San Maggie Lei , John Stewart Fabila Carrasco , Javier Escudero

We investigate the structure of connected graphs, not necessarily locally finite, with infinitely many ends. On the one hand we study end-transitive such graphs and on the other hand we study such graphs with the property that the…

Combinatorics · Mathematics 2010-03-19 Matthias Hamann

We find the asymptotic number of connected graphs with $k$ vertices and $k-1+l$ edges when $k,l$ approach infinity, reproving a result of Bender, Canfield and McKay. We use the {\em probabilistic method}, analyzing breadth-first search on…

Combinatorics · Mathematics 2007-05-23 Remco van der Hofstad , Joel Spencer

We study separating systems of the edges of a graph where each member of the separating system is a path. We conjecture that every $n$-vertex graph admits a separating path system of size $O(n)$ and prove this in certain interesting special…

The problem of finding paths in temporal graphs has been recently considered due to its many applications. In this paper we consider a variant of the problem that, given a vertex-colored temporal graph, asks for a path whose vertices have…

Data Structures and Algorithms · Computer Science 2021-09-06 Riccardo Dondi , Mohammad Mehdi Hosseinzadeh

This article introduces a model for interacting vertex-reinforced random walks, each taking values on a complete sub-graph of a locally finite undirected graph. The transition probability for a walk to a given vertex depends on the…

Probability · Mathematics 2025-08-25 Fernando P. A. Prado , Rafael A. Rosales

In this paper we generalise the notion of visibility from a point in an integer lattice to the setting of graph theory. For a vertex $x$ of a connected graph $G$, we say that a set $S \subseteq V(G)$ is an \emph{$x$-position set} if for any…

The betweenness centrality of a vertex v is an important centrality measure that quantifies how many optimal paths between pairs of other vertices visit v. Computing betweenness centrality in a temporal graph, in which the edge set may…

Data Structures and Algorithms · Computer Science 2023-02-23 Jessica Enright , Kitty Meeks , Hendrik Molter

Despite the celebrated popularity of Graph Neural Networks (GNNs) across numerous applications, the ability of GNNs to generalize remains less explored. In this work, we propose to study the generalization of GNNs through a novel…

Machine Learning · Computer Science 2024-04-17 Shouheng Li , Dongwoo Kim , Qing Wang