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In usual scale-free networks of Barabasi-Albert type, a newly added node selects randomly m neighbors from the already existing network nodes, proportionally to the number of links these had before. Then the number N(k) of nodes with k…

Computational Physics · Physics 2015-06-04 M. A. Sumour , M. A. Radwan

The scale-free model of Barabasi and Albert gave rise to a burst of activity in the field of complex networks. In this paper, we revisit one of the main assumptions of the model, the preferential attachment rule. We study a model in which…

Statistical Mechanics · Physics 2009-11-10 Jesus Gomez-Gardenes , Yamir Moreno

We consider an inhomogeneous Erd\H{o}s-R\'enyi random graph ensemble with exponentially decaying random disconnection probabilities determined by an i.i.d. field of variables with heavy tails and infinite mean associated to the vertices of…

Probability · Mathematics 2026-04-01 Luca Avena , Diego Garlaschelli , Rajat Subhra Hazra , Margherita Lalli

Many real-world networks of interest are embedded in physical space. We present a new random graph model aiming to reflect the interplay between the geometries of the graph and of the underlying space. The model favors configurations with…

Probability · Mathematics 2017-06-14 Jean-Christophe Mourrat , Daniel Valesin

In this paper we generalize the concept of random networks to describe networks with non trivial features by a statistical mechanics approach. This framework is able to describe ensembles of undirected, directed as well as weighted…

Disordered Systems and Neural Networks · Physics 2009-11-13 Ginestra Bianconi

In this paper we calculate the average number of cliques in random scale-free networks. We consider first the hidden variable ensemble and subsequently the Molloy Reed ensemble. In both cases we find that cliques, i.e. fully connected…

Statistical Mechanics · Physics 2009-11-11 Ginestra Bianconi , Matteo Marsili

We consider the length of {\em ordered loose paths} in the random $r$-uniform hypergraph $H=H^{(r)}(n, p)$. A ordered loose path is a sequence of edges $E_1,E_2,\ldots,E_\ell$ where $\max\{j\in E_i\}=\min\{j\in E_{i+1}\}$ for $1\leq…

Combinatorics · Mathematics 2026-04-03 Andrzej Dudek , Alan Frieze , Wesley Pegden

We show how appropriate rewiring with the aid of Metropolis Monte Carlo computational experiments can be exploited to create network topologies possessing prescribed values of the average path length (APL) while keeping the same…

Social and Information Networks · Computer Science 2023-03-24 Andreas I. Reppas , Konstantinos Spiliotis , Constantinos I. Siettos

In the paper, we study fluctuations over several ensembles of maximum-entropy random networks. We derive several fluctuation-dissipation relations characterizing susceptibilities of different networks to changes in external fields. In the…

Disordered Systems and Neural Networks · Physics 2009-11-11 Agata Fronczak , Piotr Fronczak , Janusz A. Holyst

Optimal paths connecting randomly selected network nodes and fixed routers are studied analytically in the presence of non-linear overlap cost that penalizes congestion. Routing becomes increasingly more difficult as the number of selected…

Disordered Systems and Neural Networks · Physics 2012-05-15 Chi Ho Yeung , David Saad

In the context of a random walk on an undirected graph, Kemeny's constant can measure the average travel time for a random walk between two randomly chosen vertices. We are interested in graphs that behave counter-intuitively in regard to…

Combinatorics · Mathematics 2022-05-18 Sooyeong Kim

Scale-free networks contain many small cliques and cycles. We model such networks as inhomogeneous random graphs with regularly varying infinite-variance weights. For these models, the number of cliques and cycles have exact integral…

Probability · Mathematics 2019-03-27 A. J. E. M. Janssen , Johan S. H. van Leeuwaarden , Seva Shneer

We propose a novel Bayesian methodology which uses random walks for rapid inference of statistical properties of undirected networks with weighted or unweighted edges. Our formalism yields high-accuracy estimates of the probability…

Physics and Society · Physics 2018-07-25 Willow B. Kion-Crosby , Alexandre V. Morozov

In this paper we study typical distances in the configuration model, when the degrees have asymptotically infinite variance. We assume that the empirical degree distribution follows a power law with exponent $\tau\in (2,3)$, up to value…

Probability · Mathematics 2017-09-20 Remco van der Hofstad , Julia Komjathy

We study numerically the mean access times for random walks on hybrid disordered structures formed by embedding scale-free networks into regular lattices, considering different transition rates for steps across lattice bonds ($F$) and…

Disordered Systems and Neural Networks · Physics 2009-11-11 Julián Candia , Paul E. Parris , V. M. Kenkre

Asymptotic properties of random graph sequences, like occurrence of a giant component or full connectivity in Erd\H{o}s-R\'enyi graphs, are usually derived with very specific choices for defining parameters. The question arises to which…

Probability · Mathematics 2024-02-20 B. J. K. Kleijn , S. Rizzelli

In this paper we present an analytic study of sampled networks in the case of some important shortest-path sampling models. We present analytic formulas for the probability of edge discovery in the case of an evolving and a static network…

Disordered Systems and Neural Networks · Physics 2013-05-29 Attila Fekete , Gábor Vattay

In the paper, we present an incremental approach in the construction of scale free networks with hidden variables. The work arises from the necessity to generate that type of networks with a given number of links instead of obtaining a…

Disordered Systems and Neural Networks · Physics 2021-06-10 Fabio Vanni

Real-world networks are neither regular nor random, a fact elegantly explained by mechanisms such as the Watts-Strogatz or the Barabasi-Albert models, among others. Both mechanisms naturally create shortcuts and hubs, which while enhancing…

Physics and Society · Physics 2023-07-03 Ernesto Estrada , Jesús Gómez-Gardeñes , Lucas Lacasa

Consider a network of agents that all want to guess the correct value of some ground truth state. In a sequential order, each agent makes its decision using a single private signal which has a constant probability of error, as well as…

Social and Information Networks · Computer Science 2024-10-08 Kevin Lu , Jordan Chong , Matt Lu , Jie Gao
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