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Can the topology of a network that consists of many particles interacting with each other change in complexity when a phase transition occurs? The answer to this question is particularly interesting to understand the nature of phase…

Statistical Mechanics · Physics 2014-08-12 Chung-Pin Chou

We study a minimal model of traffic flows in complex networks, simple enough to get analytical results, but with a very rich phenomenology, presenting continuous, discontinuous as well as hybrid phase transitions between a free-flow phase…

Statistical Mechanics · Physics 2015-05-13 Daniele De Martino , Luca Dall'Asta , Ginestra Bianconi , Matteo Marsili

Classical percolation theory underlies many processes of information transfer along the links of a network. In these standard situations, the requirement for two nodes to be able to communicate is the presence of at least one uninterrupted…

Statistical Mechanics · Physics 2023-10-25 Lorenzo Cirigliano , Claudio Castellano , Gábor Timár

We consider random networks whose dynamics is described by a rate equation, with transition rates $w_{nm}$ that form a symmetric matrix. The long time evolution of the system is characterized by a diffusion coefficient $D$. In one dimension…

Statistical Mechanics · Physics 2012-12-04 Yaron de Leeuw , Doron Cohen

We develop a theoretical approach to percolation in random clustered networks. We find that, although clustering in scale-free networks can strongly affect some percolation properties, such as the size and the resilience of the giant…

Disordered Systems and Neural Networks · Physics 2009-11-11 M. Angeles Serrano , Marian Boguna

Research on information diffusion generally assumes complete knowledge of the underlying network. However, in the presence of factors such as increasing privacy awareness, restrictions on application programming interfaces (APIs) and…

Social and Information Networks · Computer Science 2015-05-22 Václav Belák , Afra Mashhadi , Alessandra Sala , Donn Morrison

Recently much attention has been paid to the study of the robustness of interdependent and multiplex networks and, in particular, networks of networks. The robustness of interdependent networks can be evaluated by the size of a mutually…

Statistical Mechanics · Physics 2015-06-18 Ginestra Bianconi , Sergey N. Dorogovtsev

A set of discrete individual points located in an embedding continuum space can be seen as percolating or non-percolating, depending on the radius of the discs/spheres associated with each of them. This problem is relevant in theoretical…

Statistical Mechanics · Physics 2022-07-21 Pablo Villegas , Tommaso Gili , Andrea Gabrielli , Guido Caldarelli

Diffusion over a network refers to the phenomenon of a change of state of a cross-sectional unit in one period leading to a change of state of its neighbors in the network in the next period. One may estimate or test for diffusion by…

Methodology · Statistics 2022-05-17 Kyungchul Song

Percolation theory characterizing the robustness of a network has applications ranging from biology, to epidemic spreading, and complex infrastructures. Percolation theory, however, only concern the typical response of a infinite network to…

Disordered Systems and Neural Networks · Physics 2018-02-28 Ginestra Bianconi

Models of percolation processes on networks currently assume locally tree-like structures at low densities, and are derived exactly only in the thermodynamic limit. Finite size effects and the presence of short loops in real systems however…

Physics and Society · Physics 2018-12-05 Giacomo Rapisardi , Guido Caldarelli , Giulio Cimini

We present simulation results for the contact process on regular, cubic networks that are composed of a one-dimensional lattice and a set of long edges with unbounded length. Networks with different sets of long edges are considered, that…

Statistical Mechanics · Physics 2015-05-13 R. Juhász , G. Ódor

The use of machine learning algorithms to investigate phase transitions in physical systems is a valuable way to better understand the characteristics of these systems. Neural networks have been used to extract information of phases and…

Neural and Evolutionary Computing · Computer Science 2025-10-21 Rodrigo Carmo Terin , Zochil González Arenas , Roberto Santana

Many-body systems when continuous phase transition occurs are mainly built in the interrelationship between particles, implemented through many-body correlations. Some of them may exhibit so-called topological order hardly measured by…

Statistical Mechanics · Physics 2015-06-17 Chung-Pin Chou , Yi-Hua Wang , Ming-Chiang Chung

Intercellular exchange networks are essential for the adaptive capabilities of populations of cells. While diffusional exchanges have traditionally been difficult to map, recent advances in nanotechnology enable precise probing of exchange…

Statistical Mechanics · Physics 2024-12-13 Luís C. F. Latoski , Andrea De Martino , Daniele De Martino

Analysis of degree-degree dependencies in complex networks, and their impact on processes on networks requires null models, i.e. models that generate uncorrelated scale-free networks. Most models to date however show structural negative…

Physics and Society · Physics 2015-08-12 Pim van der Hoorn , Nelly Litvak

Controlled experimental studies of percolation are challenging due to difficulties in tuning site connectivity, isolating local interactions, and mitigating finite-size effects. In this work, we experimentally investigate percolation with a…

Social fragmentation transition is a transition of social states between many disconnected communities with distinct opinions and a well-connected single network with homogeneous opinions. This is a timely research topic with high relevance…

Social and Information Networks · Computer Science 2023-04-28 Hiroki Sayama

One of the challenging problems in the condensed matter physics is to understand the quantum many-body systems, especially, their physical mechanisms behind. Since there are only a few complete analytical solutions of these systems, several…

Statistical Mechanics · Physics 2020-03-27 Jozef Genzor , Tomotoshi Nishino , Andrej Gendiar

We propose a systematic methodology to identify the topological phase transition through a self-supervised machine learning model, which is trained to correlate system parameters to the non-local observables in time-of-flight experiments of…

Quantum Gases · Physics 2021-09-01 Chi-Ting Ho , Daw-Wei Wang
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