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We present analytical results for the distribution of the number of cycles in directed and undirected random 2-regular graphs (2-RRGs) consisting of $N$ nodes. In directed 2-RRGs each node has one inbound link and one outbound link, while…

Statistical Mechanics · Physics 2023-03-01 Ido Tishby , Ofer Biham , Eytan Katzav , Reimer Kühn

We study models of weighted exponential random graphs in the large network limit. These models have recently been proposed to model weighted network data arising from a host of applications including socio-econometric data such as migration…

Probability · Mathematics 2018-07-12 Shankar Bhamidi , Suman Chakraborty , Skyler Cranmer , Bruce Desmarais

We study directed random graphs (random graphs whose edges are directed) as they evolve in discrete time by the addition of nodes and edges. For two distinct evolution strategies, one that forces the graph to a condition of near acyclicity…

Statistical Mechanics · Physics 2007-05-23 Valmir C. Barbosa , Raul Donangelo , Sergio R. Souza

Distributions of the size of the largest component, in particular the large-deviation tail, are studied numerically for two graph ensembles, for Erdoes-Renyi random graphs with finite connectivity and for two-dimensional bond percolation.…

Disordered Systems and Neural Networks · Physics 2015-05-20 A. K. Hartmann

We analyze graphs attaining the extreme values of various spectral indices in the class of all simple connected graphs, as well as in the class of graphs which are not complete multipartite graphs. We also present results on density of…

Combinatorics · Mathematics 2023-06-13 Sona Pavlikova , Daniel Sevcovic , Jozef Siran

In this paper, we study exponential random graph models subject to certain constraints. We obtain some general results about the asymptotic structure of the model. We show that there exists non-trivial regions in the phase plane where the…

Probability · Mathematics 2017-02-27 Lingjiong Zhu

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

We study the asymptotics of large directed graphs, constrained to have certain densities of edges and/or outward $p$-stars. Our models are close cousins of exponential random graph models (ERGMs), in which edges and certain other subgraph…

Probability · Mathematics 2015-08-24 David Aristoff , Lingjiong Zhu

We define a general class of network formation models, Statistical Exponential Random Graph Models (SERGMs), that nest standard exponential random graph models (ERGMs) as a special case. We provide the first general results on when these…

Physics and Society · Physics 2014-06-26 Arun G. Chandrasekhar , Matthew O. Jackson

We construct models of exactly solvable two-particle quantum graphs with certain non-local two-particle interactions, establishing appropriate boundary conditions via suitable self-adjoint realisations of the two-particle Laplacian. Showing…

Mathematical Physics · Physics 2017-02-20 Jens Bolte , George Garforth

We introduce a method for the theoretical analysis of exponential random graph models. The method is based on a large-deviations approximation to the normalizing constant shown to be consistent using theory developed by Chatterjee and…

Probability · Mathematics 2013-11-21 Sourav Chatterjee , Persi Diaconis

We introduce a statistical mechanics formalism for the study of constrained graph evolution as a Markovian stochastic process, in analogy with that available for spin systems, deriving its basic properties and highlighting the role of the…

Disordered Systems and Neural Networks · Physics 2015-05-13 A. C. C. Coolen , A. De Martino , A. Annibale

Graph dynamical systems (GDSs) can be used to describe a wide range of distributed, nonlinear phenomena. In this paper we characterize cycle equivalence of a class of finite GDSs called sequential dynamical systems SDSs. In general, two…

Dynamical Systems · Mathematics 2009-11-13 Matthew Macauley , Henning S. Mortveit

We compute spectra of large stochastic matrices $W$, defined on sparse random graphs, where edges $(i,j)$ of the graph are given positive random weights $W_{ij}>0$ in such a fashion that column sums are normalized to one. We compute spectra…

Disordered Systems and Neural Networks · Physics 2015-06-23 Reimer Kuehn

We study the tailoring of structured random graph ensembles to real networks, with the objective of generating precise and practical mathematical tools for quantifying and comparing network topologies macroscopically, beyond the level of…

Disordered Systems and Neural Networks · Physics 2015-05-13 A. Annibale , A. C. C. Coolen , L. P. Fernandes , F. Fraternali , J. Kleinjung

We study both numerically and analytically what happens to a random graph of average connectivity "alpha" when its leaves and their neighbors are removed iteratively up to the point when no leaf remains. The remnant is made of isolated…

Statistical Mechanics · Physics 2009-11-07 M. Bauer , O. Golinelli

The energy levels of a quantum graph with time reversal symmetry and unidirectional classical dynamics are doubly degenerate and obey the spectral statistics of the Gaussian Unitary Ensemble. These degeneracies, however, are lifted when the…

Mathematical Physics · Physics 2015-08-20 Maram Akila , Boris Gutkin

The spectral and localization properties of heterogeneous random graphs are determined by the resolvent distributional equations, which have so far resisted an analytic treatment. We solve analytically the resolvent equations of random…

Disordered Systems and Neural Networks · Physics 2022-12-27 Jeferson D. Silva , Fernando L. Metz

Random unimodular lattice triangulations have been recently used as an embedded random graph model, which exhibit a crossover behaviour between an ordered, large-world and a disordered, small-world behaviour. Using the ergodic Pachner flips…

Disordered Systems and Neural Networks · Physics 2016-06-22 Benedikt Krüger , Ella M. Schmidt , Klaus Mecke

We give several results showing that different discrete structures typically gain certain spanning substructures (in particular, Hamilton cycles) after a modest random perturbation. First, we prove that adding linearly many random edges to…

Combinatorics · Mathematics 2018-03-23 Michael Krivelevich , Matthew Kwan , Benny Sudakov
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