Showing papers by "H. Eugene Stanley published in 2021"

Multilayer information spillover networks: measuring interconnectedness of financial institutions

Abstract: We propose multilayer information spillover networks to measure the interconnectedness of financial institutions by comprehensively considering mean spillover layer, volatility spillover layer and ...

Optimal resilience of modular interacting networks.

Abstract: Coupling between networks is widely prevalent in real systems and has dramatic effects on their resilience and functional properties. However, current theoretical models tend to assume homogeneous coupling where all the various subcomponents interact with one another, whereas real-world systems tend to have various different coupling patterns. We develop two frameworks to explore the resilience of such modular networks, including specific deterministic coupling patterns and coupling patterns where specific subnetworks are connected randomly. We find both analytically and numerically that the location of the percolation phase transition varies nonmonotonically with the fraction of interconnected nodes when the total number of interconnecting links remains fixed. Furthermore, there exists an optimal fraction [Formula: see text] of interconnected nodes where the system becomes optimally resilient and is able to withstand more damage. Our results suggest that, although the exact location of the optimal [Formula: see text] varies based on the coupling patterns, for all coupling patterns, there exists such an optimal point. Our findings provide a deeper understanding of network resilience and show how networks can be optimized based on their specific coupling patterns.

Measuring the systemic risk in indirect financial networks

Abstract: In this study, we present a novel measurement approach for systemic risk by considering an indirect network structure. In a departure from previous studies, this measurement method captures spillov...

Gravity model in dockless bike-sharing systems within cities.

Abstract: Due to previous technical challenges with the collection of data on riding behaviors, there have only been a few studies focusing on patterns and regularities of biking traffic, which are crucial to understand to help achieve a greener and more sustainable future urban development. Recently, with the booming of the sharing economy, and the development of the Internet of Things (IoT) and mobile payment technology, dockless bike-sharing systems that record information for every trip provide us with a unique opportunity to study the patterns of biking traffic within cities. We first reveal a spatial scaling relation between the cumulative volume of riding activities and the corresponding distance to the city center, and a power law distribution on the volume of biking flows between fine-grained locations in both Beijing and Shanghai. We validate the effectiveness of the general gravity model on predicting biking traffic at fine spatial resolutions, where population-related parameters are less than unity, indicating that smaller populations are relatively more important per capita in generating biking traffic. We then further study the impacts of spatial scale on the gravity model and reveal that the distance-related parameter grows in a similar way as population-related parameters when the spatial scale of the locations increases. In addition, the flow patterns of some special locations (sources and sinks) that cannot be fully explained by the gravity model are studied.

Assessing the Attraction of Cities on Venture Capital From a Scaling Law Perspective

Abstract: Cities are centers for the integration of capital and incubators of inventions. Attracting venture capital (VC) is of great importance for cities to advance in innovative technology and business models towards a sustainable and prosperous future. Yet we still lack a quantitative understanding of the relationship between urban characteristics and VC activities. In this paper, we find a clear nonlinear scaling relationship between VC activities and the urban population of Chinese cities. In such nonlinear systems, the widely applied linear per capita indicators would be either biased to larger cities or smaller cities depends on whether it is superlinear or sublinear, while the residual of cities relative to the prediction of scaling law is a more objective and scale-invariant metric. Such a metric can distinguish the effects of local dynamics and scaled growth induced by the change of population size. The spatiotemporal evolution of such metrics on VC activities reveals three distinct groups of cities, two of which stand out with increasing and decreasing trends, respectively. The taxonomy results together with spatial analysis also signify different development modes between large urban agglomeration regions. Besides, we notice the evolution of scaling exponents on VC activities are of much larger fluctuations than on socioeconomic output of cities, and a conceptual model that focuses on the growth dynamics of different sized cities can well explain it, which we assume would be general to other scenarios.

Three-State Majority-Vote Model on Small-World Networks

Abstract: In this work, we study the opinion dynamics of the three-state majority-vote model on small-world networks of social interactions. In the majority-vote dynamics, an individual adopts the opinion of the majority of its neighbors with probability 1−q, and a different opinion with chance q, where q stands for the noise parameter. The noise q acts as a social temperature, inducing the dissensus among individual opinions. With probability p, we rewire the connections of the two-dimensional square lattice network, allowing long-range interactions in the society, thus yielding the small-world property present in many different real-world systems. We employ Monte Carlo simulations to investigate the second-order phase transition of the system, and obtain the critical noise qc, as well as the standard critical exponents β/ν, γ/ν, and 1/ν for several values of the rewiring probability p. We conclude that the rewiring of the lattice enhances the social order in the system and drives the model to different universality classes from that of the three-state majority-vote model in two-dimensional square lattices.

The impact of memory effect on resonance behavior in a fractional oscillator with small time delay

Abstract: We study stochastic resonance (SR) phenomenon in the fractional oscillator with time delay and damping fluctuation, analyze the impact of time delay and fractional damping as two memory ingredients on SR, and put forward firstly the concept of the robustness of GSR. By moment method, we obtain the analytical expression of the output amplitude gain and find that fluctuations in the output amplitude gain are non-monotonic. Using numerical simulations we verify the accuracy of the analytical results. We find (i) that the length of time delay and system order are parameters related to memory; (ii) that the output amplitude gain could attain a maximum by increasing driving frequency close to system frequency, and small time delay and system order contribute to enhance the resonance intensity; (iii) that the evolution of the output amplitude gain versus the noise intensity exhibits one-peak resonance, and small time delay can enhance the resonance intensity and the robustness of SR regarding to driving frequency and system frequency; (iv) that the evolution of the output amplitude gain versus the noise correlation rate presents one-peak resonance in the presence of small time delay, critical time delay is bigger with the increasing system order when noise intensity is fixed and critical time delay is smaller with the increasing noise intensity when system order is fixed.

A Spatio-Temporal Co-Clustering Framework for Discovering Mobility Patterns: A Study of Manhattan Taxi Data

Abstract: Research on clustering spatio-temporal data to extract mobility patterns requires further development, as most existing studies do not simultaneously integrate data along both spatial dimensions and temporal dimensions but instead focus on only one dimension or separate the dimensions in analyses and applications, which could lead to discoveries that are not representative of the overall data or are dificult to interpret. To simultaneously reveal the spatial and temporal patterns of urban mobility datasets, we propose an analytical framework that is based on co-clustering and enables mobility behaviors to be distinguished in spatial and temporal dimensions. We use one month of taxi GPS data from the Manhattan area to explore spatio-temporal co-occurrence patterns. The spatial and temporal dimensions of taxi trip data were co-clustered by using the Bregman Block Average co-clustering algorithm with I-divergence (BBAC_I). We performed this process on weekdays and holidays and compared the mobility differences between these two periods. The experimental results demonstrated the effectiveness of this analytical framework, with which we can reveal the spatial patterns and their temporal dynamics as well as temporal patterns and their spatial dynamics in mobility data.

Majority-vote model with limited visibility: An investigation into filter bubbles

Abstract: The dynamics of opinion formation in a society is a complex phenomenon where many variables play essential roles. Recently, the influence of algorithms to filter which content is fed to social networks users has come under scrutiny. Supposedly, the algorithms promote marketing strategies, but can also facilitate the formation of filters bubbles in which a user is most likely exposed to opinions that conform to their own. In the two-state majority-vote model, an individual adopts an opinion contrary to the majority of its neighbors with probability q , defined as the noise parameter. Here, we introduce a visibility parameter V in the dynamics of the majority-vote model, which equals the probability of an individual ignoring the opinion of each one of its neighbors. For V = 0 . 5 each individual will, on average, ignore the opinion of half of its neighboring nodes. We employ Monte Carlo simulations to calculate the critical noise parameter as a function of the visibility q c ( V ) and obtain the phase diagram of the model. We find that the critical noise is an increasing function of the visibility parameter, such that a lower value of V favors dissensus. Via finite-size scaling analysis we obtain the critical exponents of the model, which are visibility-independent, and show that the model belongs to the Ising universality class. We compare our results to the case of a network submitted to a static site dilution and find that the limited visibility model is a more subtle way of inducing opinion polarization in a social network.

Percolation on coupled networks with multiple effective dependency links.

Abstract: The ubiquitous coupled relationship between network systems has become an essential paradigm to depict complex systems A remarkable property in the coupled complex systems is that a functional node should have multiple external support associations in addition to maintaining the connectivity of the local network In this paper, we develop a theoretical framework to study the structural robustness of the coupled network with multiple useful dependency links It is defined that a functional node has the broadest connectivity within the internal network and requires at least M support link of the other network to function In this model, we present exact analytical expressions for the process of cascading failures, the fraction of functional nodes in the stable state, and provide a calculation method of the critical threshold The results indicate that the system undergoes an abrupt phase transition behavior after initial failure Moreover, the minimum inner and inter-connectivity density to maintain system survival is graphically presented at different multiple effective dependency links Furthermore, we find that the system needs more internal connection densities to avoid collapse when it requires more effective support links These findings allow us to reveal the details of a more realistic coupled complex system and develop efficient approaches for designing resilient infrastructure

A Novel Causal Risk-Based Decision-Making Methodology: The Case of Coronavirus.

Abstract: Either in the form of nature's wrath or a pandemic, catastrophes cause major destructions in societies, thus requiring policy and decisionmakers to take urgent action by evaluating a host of interdependent parameters, and possible scenarios. The primary purpose of this article is to propose a novel risk-based, decision-making methodology capable of unveiling causal relationships between pairs of variables. Motivated by the ongoing global emergency of the coronavirus pandemic, the article elaborates on this powerful quantitative framework drawing on data from the United States at the county level aiming at assisting policy and decision makers in taking timely action amid this emergency. This methodology offers a basis for identifying potential scenarios and consequences of the ongoing 2020 pandemic by drawing on weather variables to examine the causal impact of changing weather on the trend of daily coronavirus cases.

Three transport models for charged particles in three-dimensional semiconductors driven by a fractional noise

Abstract: In this work, three models of the motion of charged particles in three-dimensional semiconductors governed by a stochastic differential equation driven by a magnetic field and an intrinsic fractional Gaussian noise have been introduced. Based on the general expansion theorem for the Laplace transform, the expressions of the average position and the average velocity of a charged particle have been obtained. The expressions of the complex susceptibilities, the spectral amplification, the stationary form of current density, as well as power absorption also have been obtained. It is worthy to note that the cyclotron resonance, stochastic dynamics of a charged particle could be induced by fractional noise. Furthermore, the expressions of variances and the generalized Fokker–Planck equation (GFPE) for the non-Markovian dynamics also have been investigated.

Non-Markovian thermal-bath-induced Brownian motion in velocity space in the presence of a magnetic field at arbitrary direction

Abstract: In this work, from the perspective of statistical mechanics, the statistical properties of charged-particle motion in a microwave field and a magnetic field with a general direction described by a generalized Langevin equation subjected to an intrinsic noise with a power-law time decay correlation function have been studied. Using the general expansion theorem for the Laplace transform, the drift velocity of a charged particle in three directions can be expressed in terms of the relaxation functions. Based on the linear response theory, the expression of the complex susceptibilities, the spectral amplification, the stationary form of current density, and the power absorption have been obtained. It is noteworthy that the stochastic dynamics of a charged particle could be induced by fractional Gaussian noise. Additionally, the variances and covariances of charged particles have been studied based on the relations between relaxation functions and memory kernel functions.

A new look at calendar anomalies: Multifractality and day of the week effect

Abstract: Stock markets can become inefficient due to calendar anomalies known as day-of-the-week effect. Calendar anomalies are well-known in financial literature, but the phenomena remain to be explored in econophysics. In this paper we use multifractal analysis to evaluate if the temporal dynamics of market returns also exhibits calendar anomalies such as day-of-the-week effects. We apply the multifractal detrended fluctuation analysis (MF-DFA) to daily returns of market indices around the world for each day of the week. Our results indicate that individual days of the week are characterized by distinct multifractal properties. Monday returns tend to exhibit more persistent behavior and richer multifractal structures than other day-resolved returns. Shuffling the series reveals that multifractality arises both from a broad probability density function and from long-term correlations. From the time-dependent multifractal analysis we find that multifractal spectra for Monday returns are much wider than for other days during periods of financial crises. The presence of day-of-the-week effects in multifractal dynamics of market returns motivates further research on calendar anomalies from an econophysics perspective.

Short-time Monte Carlo simulation of the majority-vote model on cubic lattices

Abstract: We perform short-time Monte Carlo simulations to study the criticality of the isotropic two-state majority-vote model on cubic lattices of volume N = L 3 , with L up to 2048. We obtain the precise location of the critical point by examining the scaling properties of a new auxiliary function Ψ . We perform finite-time scaling analysis to accurately calculate the whole set of critical exponents, including the dynamical critical exponent z = 2 . 027 ( 9 ) , and the initial slip exponent θ = 0 . 1081 ( 1 ) . Our results indicate that the majority-vote model in three dimensions belongs to the same universality class of the three-dimensional Ising model.

Diffusion interactions between crossing fibers of the brain.

Abstract: Purpose Recent observations of several preferred orientations of diffusion in deep white matter may indicate either (a) that axons in different directions are independently bundled in thick sheets and function noninteractively, or more interestingly, (b) that the axons are closely interwoven and would exhibit branching and sharp turns. This study aims to investigate whether the dependence of dMRI Q-ball signal on the interpulse time Δ can decode the smaller-than-voxel-size brain structure, in particular, to distinguish scenarios (a) and (b). Methods High-resolution Q-ball images of a healthy brain taken with b = 8000 s/mm2 for 3 different values of Δ were analyzed. The exchange of water molecules between crossing fibers was characterized by the fourth Fourier coefficient f 4 ( Δ ) of the signal profile in the plane of crossing. To interpret the empirical results, a model consisting of differently oriented parallel sheets of cylinders was developed. Diffusion of water molecules inside and outside cylinders was simulated by the Monte Carlo method. Results Simulations predict that f 4 ( Δ ) , agreeing with the empirical results, must increase with Δ for large b-values, but may peak at a typical Δ that depends on the thickness of the cylinder sheets for intermediate b-values. Thus, the thickness of axon layers in voxels with 2 predominant orientations can be detected from empirical f 4 ( Δ ) taken at smaller b-values. Conclusion Based on the simulation results, recommendations are made on how to design a dMRI experiment with optimal b-value and range of Δ in order to measure the thickness of axon sheets in the white matter, hence to distinguish (a) and (b).