Paulo Freitas Gomes

Visibility graphs are spatial interpretations of time series. When derived from the time evolution of physical systems, the graphs associated with such series may exhibit properties that can reflect aspects such as ergodicity, criticality, or other dynamical behaviors. It is important to describe how the criticality of a system is manifested in the structure of the corresponding graphs or, in a particular way, in the spectra of certain matrices constructed from them. In this paper, we show how the critical behavior of an Ising spin system manifests in the spectra of the adjacency and Laplacian matrices constructed from an ensemble of time evolutions simulated via Monte Carlo (MC) Markov chains, even for small systems and short MC steps. In particular, we show that the number of spanning trees-or its logarithm-which represents a kind of structural entropy or topological complexity here obtained from Kirchhoff's theorem, can, in an alternative way, describe the criticality of the spin system. These findings parallel those obtained from the spectra of correlation matrices, which similarly encode signatures of critical and chaotic behavior.

In this work, we investigated how the use of complex networks as catalytic surfaces can affect the phase diagram of the Yaldram–Khan model, as well as how the order of the phase transitions present in the seminal work behaves when randomness is added to the model. The study was conducted by taking into consideration two well‐known random networks, the Erdős‐Rényi network (ERN), with its long‐range randomness, and the random geometric graph (RGG), with its spatially constrained randomness. We perform extensive steady‐state Monte Carlo simulations for , the NO dissociation rate, and show the behavior of the reactive window as a function of the average degree of the networks. Our results also show that, different from the ERN, which preserves the nature of the phase transitions of the original model for all considered average degrees, the RGG seems to have two second‐order phase transitions for small values of average degree.

The contact process is a nonequilibrium Hamiltonian model that, even in one dimension, lacks an exact solution and has been extensively studied via Monte Carlo simulations, both in steady-state and time-dependent scenarios. Although the effects of particle mobility and diffusion on criticality have been preliminarily explored, they remain poorly understood in many aspects. In this work, we examine how the critical rate of the model varies with the probability of particle mobility. By analyzing different stochastic evolutions of the system, we employ two modern approaches: (1) Random Matrix Theory (RMT): By building on the success of RMT, particularly Wishart-like matrices, in studying statistical physics of systems with up-down symmetry via magnetization dynamics [R. da Silva, IJMPC 2022], we demonstrate its applicability to models with an absorbing state; (2) Optimized Temporal Power Laws: By using short-time dynamics, we optimize power laws derived from ensemble-averaged evolutions of the system. Both methods consistently reveal that the critical rate decays with mobility according to a simple Belehradek function. Additionally, a straightforward mean-field analysis supports the decay of the critical parameter with mobility, although it predicts a simpler linear dependence. We also demonstrate that the more sophisticated pair approximation mean-field model developed by ben-Avraham and Köhler aligns closely with the Belehradek function, precisely matching our lattice simulation results.

A Ciência de Redes é um formalismo matemático essencial na descrição de sistemas complexos. Este artigo é uma breve introdução dos conceitos principais da Teoria de Grafos, a base da Ciência de Redes. Começando com uma introdução histórica apresentamos os elementos básicos para definição de grafos como grau médio, matriz adjacência e componentes. Em seguida apresentamos alguns modelos básicos e famosos de redes mostrando suas definições e propriedades. Finalizamos com uma apresentação do fenômeno de percolação em rede que mostra a sinergia entre Ciência de Redes e conceitos mais tradicionais da física como transição de fase e criticalidade.

We investigate the entanglement in the ground state of systems comprising two and three qubits with random interactions. Since the Hamiltonians also contain deterministic one-body terms, by varying the interaction strength, one can continuously interpolate between deterministic separable eigenstates and fully random entangled eigenstates, with nontrivial intermediate behavior. Entanglement strongly depends on the underlying topology of the interaction among the qubits. Since W states correspond to a zero-measure set as compared to the set of Greenberger-Horne-Zeilinger (GHZ) states, in all investigated cases the ground states are of the latter type. However, for a certain class of interactions (nonseparable collective potential) high GHZ entanglement is produced, while for fully separable pairwise interactions the marginal GHZ ground states concentrate in the vicinity of W states.

The self-avoid random walk algorithm has been extensively used in the study of polymers. In this work we study the basic properties of the trajectories generated with this algorithm when two interactions are added to it: contact and folding interaction. These interactions represent the internal forces of the polymer as well as the effect of the solvent. When independently added to the algorithm, the contact interaction creates the compact phase while the folding one creates the extended phase. These are the consequences of the typical event of each interaction. On the other hand, when this typical event is avoided there is no established phase on the system. When simultaneously added, there is a competition between the interactions and the folding one is dominant over the contact one. The resulting phase is always the extended one with and without the contact interaction.