Physica A: Statistical Mechanics and its Applications最新文献_第4页

Crypto network 加密网络

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-09-25 DOI: 10.1016/j.physa.2024.130128

The empirical literature has studied linkages in the cryptocurrency market because knowing how shocks pass from one currency to another helps policymakers and practitioners better counter their propagation in these and related markets. This paper contributes to this literature by proposing a methodology based on Granger causality and network analysis. Using the daily log-returns of 22 cryptocurrencies over the period 2018–2023, I develop a VAR model to infer unidirectional or bidirectional Granger causality among cryptocurrencies. These relationships are then transformed into a directed network and several centrality measures are calculated. The centrality measures are also observed over the years to understand the dynamics of the cryptocurrency network. I find out that each one unit increase in eigencentrality is associated with a 0.22 percent increase in log-returns. Cryptocurrencies are nontrivially connected, and in this sample Cardano, Dogecoin, Gridcoin, and Neo are amongst the most central in the network throughout the period. Some cryptocurrencies, such as Dogecoin or Neo, show decreasing centrality over the years, while others, such as Gridcoin, Litecoin, Namecoin, or Ripple, gain centrality. These results support the idea that the cryptocurrency market is no longer exclusively associated with Bitcoin and lay the groundwork for further study of shock propagation in financial markets.

实证文献对加密货币市场的联系进行了研究，因为了解冲击如何从一种货币传递到另一种货币，有助于政策制定者和从业者更好地应对冲击在这些市场和相关市场的传播。本文提出了一种基于格兰杰因果关系和网络分析的方法，为这一文献做出了贡献。利用 2018-2023 年期间 22 种加密货币的每日对数收益率，我建立了一个 VAR 模型来推断加密货币之间的单向或双向格兰杰因果关系。然后将这些关系转化为有向网络，并计算出若干中心度量。为了了解加密货币网络的动态，我们还对这些中心度量进行了多年观察。我发现，特征中心度每增加一个单位，对数回报率就会增加 0.22%。加密货币是非垂直连接的，在本样本中，Cardano、Dogecoin、Gridcoin 和 Neo 是整个期间网络中最中心的加密货币。一些加密货币（如 Dogecoin 或 Neo）的中心地位逐年下降，而其他加密货币（如 Gridcoin、莱特币、Namecoin 或 Ripple）的中心地位则不断上升。这些结果支持了加密货币市场不再只与比特币相关联的观点，并为进一步研究金融市场的冲击传播奠定了基础。

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引用次数: 0

Jensen-Detrended Cross-Correlation function for non-stationary time series with application to Latin American stock markets 非平稳时间序列的詹森-去趋势交叉相关函数在拉丁美洲股票市场中的应用

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-09-25 DOI: 10.1016/j.physa.2024.130115

Variance has an important role in statistics and information theory fields, by forming the basis for many well-known information measures. Based on Jensen’s inequality and variance, the Jensen-variance information has been previously proposed to measure the distance between two random variables. Jensen-variance distance is based on the convexity property of random variable variance. Based on the relationship between Jensen-variance distance and classical Detrended Cross-Correlation (DCC) of two not necessarily stationary process, the Jensen-Detrended Covariance and Jensen-DCC functions are proposed in this paper. Moreover, Jensen-DCC function is also considered for Hénon and Logistic chaotic maps for simulated time series. Then we considered a stock market time series dataset for the study of similarity of Latin American indexes with S&P500 and Shanghai ones. We obtained a useful tool to study the similarity or distance of two non-stationary time series based on DCC coefficient.

方差在统计学和信息论领域具有重要作用，是许多著名信息度量的基础。在詹森不等式和方差的基础上，之前有人提出了詹森-方差信息来度量两个随机变量之间的距离。詹森方差距离是基于随机变量方差的凸性特性。根据两个不一定静止过程的詹森方差距离与经典的去趋势交叉相关（DCC）之间的关系，本文提出了詹森-去趋势协方差函数和詹森-DCC 函数。此外，Jensen-DCC 函数还考虑了用于模拟时间序列的 Hénon 和 Logistic 混沌图。然后，我们考虑了一个股票市场时间序列数据集，用于研究拉丁美洲指数与 S&P500 和上海指数的相似性。我们获得了一个基于 DCC 系数研究两个非平稳时间序列的相似性或距离的有用工具。

引用次数: 0

An innovative defense strategy against targeted spreading in complex networks 针对复杂网络定向传播的创新防御策略

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-09-24 DOI: 10.1016/j.physa.2024.130120

Protecting target nodes in complex networks is a critical issue in network security research. In many real-world scenarios, the identities of certain target nodes remain unknown, and the impact of this incomplete information on appropriately selecting initial spreaders is not fully understood. This paper first examines how the observability rate of target nodes affects the effectiveness of targeted spreading. The findings indicate that even if most target nodes are unobservable, identifying the optimal spreader for targeted propagation is still feasible in many real-world networks. This indicates that solely relying on protecting target nodes through external observation avoidance is insufficient. To address this issue, we developed a novel camouflage defense strategy for target nodes in complex networks by integrating target centrality, the distribution of target node groups, and the network distance between disguised and hidden target nodes. This strategy effectively hinders attackers’ selection of the optimal initial spreader by adjusting the visibility of selected target nodes and their neighbors, without altering the network structure. Finally, we validate the effectiveness of the proposed model in three aspects: matching accuracy of the optimal initial spreader, implementation of SIR propagation dynamics, and comparative testing against other models. These results were obtained not only from three types of generic artificial networks but also from multiple real datasets.

保护复杂网络中的目标节点是网络安全研究中的一个关键问题。在现实世界的许多场景中，某些目标节点的身份仍然未知，而这种不完整信息对适当选择初始传播者的影响尚未完全明了。本文首先研究了目标节点的可观察率如何影响定向传播的效果。研究结果表明，即使大多数目标节点是不可观测的，在许多真实世界的网络中，确定定向传播的最佳传播者仍然是可行的。这表明，仅仅依靠避免外部观察来保护目标节点是不够的。针对这一问题，我们通过整合目标中心性、目标节点群分布以及伪装目标节点与隐藏目标节点之间的网络距离，开发了一种新型的复杂网络目标节点伪装防御策略。该策略在不改变网络结构的前提下，通过调整所选目标节点及其邻居的可见性，有效阻止攻击者选择最优初始传播者。最后，我们从三个方面验证了所提模型的有效性：最佳初始传播者的匹配精度、SIR 传播动态的实现以及与其他模型的对比测试。这些结果不仅来自三种通用人工网络，也来自多个真实数据集。

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引用次数: 0

Synchronization on complex dynamical networks via intermittently sampled-data pinning control 通过间歇采样数据钉控在复杂动态网络上实现同步

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-09-24 DOI: 10.1016/j.physa.2024.130109

Each specific control strategy has a unique advantage, and combining multiple control strategies can harness the advantages of these strategies. Designing new control strategies by integrating different control strategies is an interesting and challenging topic. This paper introduces an intermittently sampled-data pinning (ISP) control strategy, which merges intermittent control, sampled-data control and pinning control, to study synchronization on complex dynamical networks. The ISP control strategy is proposed to solve three difficulties: first, the controllers transmitting feedback signals may be discontinuous; second, the controllers often cannot operate continuously in practical applications; third, it is usually hard to control all nodes in a dynamical network as the network size is huge. Sufficient conditions are obtained for realizing synchronization on dynamical networks. Furthermore, time delays are incorporated into the proposed control strategy to address the untimely reception of feedback signals and achieve the synchronization conditions on dynamical networks. Finally, two numerical examples demonstrate the effectiveness of the proposed control method.

每种特定的控制策略都有其独特的优势，而将多种控制策略结合在一起则可以利用这些策略的优势。通过整合不同的控制策略来设计新的控制策略是一个既有趣又具有挑战性的课题。本文介绍了一种间歇采样数据引脚（ISP）控制策略，它融合了间歇控制、采样数据控制和引脚控制，用于研究复杂动态网络的同步问题。ISP 控制策略的提出解决了三个难题：第一，传输反馈信号的控制器可能是不连续的；第二，控制器在实际应用中往往不能连续工作；第三，由于网络规模巨大，通常很难控制动态网络中的所有节点。本文提出了实现动态网络同步的充分条件。此外，在所提出的控制策略中加入了时间延迟，以解决反馈信号接收不及时的问题，实现动态网络的同步条件。最后，两个数值实例证明了所提控制方法的有效性。

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引用次数: 0

Corrigendum to “Exploring determinants of feeder mode choice behavior using Artificial Neural Network: Evidences from Delhi metro” [Phys. A: Stat. Mech. Appl. 598 (2022) 127363] 使用人工神经网络探索支线模式选择行为的决定因素：来自德里地铁的证据" [Phys. A: Stat. Mech. Appl.

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-09-24 DOI: 10.1016/j.physa.2024.130112

引用次数: 0

Impact of local navigation rules on biased random walks in multiplex Markov chains 局部导航规则对多路马尔可夫链中偏向随机游走的影响

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-09-24 DOI: 10.1016/j.physa.2024.130122

Our investigation centres on assessing the importance of a biased parameter (

α

) in a multiplex Markov chain (MMC) model that is characterized by biased random walks in multiplex networks. We explore how varying complex network topologies affect the total multiplex imbalance as a function of biased parameter. Our primary finding is that the system demonstrates a gradual increase in total imbalance within both positive and negative regions of the biased parameter, with a consistent minimum value occurring at

α = - 1

. In contrast to the negative region, the total imbalance is consistently high when

α

is significantly positive. We perform a detailed examination of four different network structures and establish three sets of multiplex networks. In each of these networks, the second layer consists of a Regular Random network, while the first layer is either a Barabási–Albert, Erdős-Rényi, or Watts Strogatz network, depending on the set. Our results demonstrate that the combination of Barabási–Albert and Random Regular Network exhibits the highest level of right saturation imbalance. Additionally, for left saturation imbalance, the Erdős–Rényi and Random Regular combination achieve a slightly higher value. We also observe that the total amount of imbalance at

α = - 1

follows a decreasing trend as the size of the network of each layer increases. Furthermore, we are also able to illustrate that the second most significant eigenvalue of the supra-transition matrix exhibits a similar pattern in response to changes in the bias parameter, aligning with the overall system’s imbalance.

我们的研究重点是评估多路马尔可夫链（MMC）模型中偏置参数（α）的重要性，该模型的特点是多路网络中的偏置随机行走。我们探讨了不同的复杂网络拓扑结构如何影响作为偏置参数函数的总复用不平衡。我们的主要发现是，在偏置参数的正负两个区域内，系统的总不平衡度都会逐渐增加，在 α=-1 时出现一致的最小值。与负值区域相反，当 α 显著为正值时，总失衡度一直很高。我们对四种不同的网络结构进行了详细研究，并建立了三组多路复用网络。在每一组网络中，第二层都由常规随机网络组成，而第一层则是巴拉巴西-阿尔伯特网络、厄尔多斯-雷尼网络或瓦特-斯特罗加茨网络，具体取决于哪一组网络。我们的结果表明，Barabási-Albert 和随机正则网络的组合表现出最高水平的右饱和不平衡。此外，就左侧饱和不平衡而言，厄尔多斯-雷尼网络和随机正则网络的组合达到了稍高的值。我们还观察到，随着各层网络规模的增加，α=-1 时的不平衡总量呈下降趋势。此外，我们还能说明，超过渡矩阵的第二个最重要特征值对偏置参数变化的响应也呈现出类似的模式，与整个系统的不平衡性相一致。

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引用次数: 0

Improving community detection in blockmodel by distance-based observation selection 通过基于距离的观测选择改进块模型中的群落检测

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-09-24 DOI: 10.1016/j.physa.2024.130125

Community detection is an important research topic in complex systems and has plenty of applications in real-world networks. Probabilistic methods, such as the Expectation–Maximization (EM), are developed to classify nodes that have similar connection patterns in a network based on blockmodels. However, the detection procedures in these models are typically started from randomly generated initial community distributions without prior knowledge. In biological and social networks, there are practical measures to obtain prior knowledge for a subset of nodes, such as local observations. These facts lead us to question how we can select a subset of nodes with known community labels to enhance the accuracy of the EM method. The current selection methods lack the relationship between detection accuracy and structural characteristics and most approaches consider the nodes as the center of communities, which is not suitable for block models. In this paper, we first study the relationships between the structural distance and detection accuracy without prior knowledge. Then we propose a distance-based indicator to describe the performance of the observation node set in the EM method. Finally, we introduce a scoring method based on the indicator to select a partial observation set, improving the accuracy of community detection using the EM method. Empirical results from synthetic and real-world networks corroborate that the proposed indicator could contribute to a better performance in kinds of scenarios.

群落检测是复杂系统中的一个重要研究课题，在现实世界的网络中有大量应用。人们开发了一些概率方法，如期望最大化法（EM），用于根据块模型对网络中具有相似连接模式的节点进行分类。然而，这些模型中的检测程序通常是从随机生成的初始群落分布开始的，没有先验知识。在生物和社会网络中，有一些实用的方法可以获得节点子集的先验知识，例如局部观测。这些事实引发了我们的疑问：如何选择具有已知社区标签的节点子集，以提高 EM 方法的准确性？目前的选择方法缺乏检测精度与结构特征之间的关系，而且大多数方法都将节点视为群落的中心，这并不适合块模型。在本文中，我们首先研究了结构距离与检测精度之间的关系，而不需要先验知识。然后，我们提出了一种基于距离的指标来描述 EM 方法中观测节点集的性能。最后，我们引入了一种基于该指标的评分方法来选择部分观测集，从而提高使用 EM 方法进行群落检测的准确性。来自合成网络和真实世界网络的经验结果证实，所提出的指标在各种情况下都能帮助提高性能。

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引用次数: 0

Influence of initiators on the tipping point in the extended Watts model 扩展瓦特模型中启动器对临界点的影响

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-09-24 DOI: 10.1016/j.physa.2024.130123

In this paper, we study how the influence of initiators (seeds) affects the tipping point of information cascades in networks. We consider an extended version of the Watts model, in which each node is either active (i.e., having adopted an innovation) or inactive. In this extended model, the adoption threshold, defined as the fraction of active neighbors required for an inactive node to become active, depends on whether the node is a seed neighbor (i.e., connected to one or more initiators) or an ordinary node (i.e., not connected to any initiators). Using the tree approximation on random graphs, we determine the tipping point, at which the fraction of active nodes in the final state increases discontinuously with an increasing seed fraction. The occurrence of a tipping point and the scale of cascades depend on two factors: whether a giant component of seed neighbors is formed when the seed fraction is large enough to trigger cascades among seed neighbors, and whether the giant component of ordinary nodes is maintained when newly activated nodes trigger further activations among ordinary nodes. The coexistence of two giant components suggests that a tipping point can appear twice. We present an example demonstrating the existence of two tipping points when there is a gap between the adoption thresholds of seed neighbors and ordinary nodes. Monte Carlo simulations clearly show that the first cascade, occurring at a small tipping point, occurs in the giant component of seed neighbors, while the second cascade, occurring at a larger tipping point, extends into the giant component of ordinary nodes.

本文研究了发起者（种子）的影响力如何影响网络中信息级联的临界点。我们考虑了沃茨模型的扩展版本，其中每个节点要么是活跃的（即采用了创新），要么是不活跃的。在这个扩展模型中，采用阈值被定义为不活跃节点变得活跃所需的活跃邻居的分数，它取决于该节点是种子邻居（即与一个或多个发起者相连）还是普通节点（即与任何发起者都不相连）。利用随机图上的树状近似，我们确定了临界点，在临界点上，最终状态中活跃节点的比例会随着种子比例的增加而不连续地增加。临界点的出现和级联的规模取决于两个因素：当种子分数大到足以引发种子邻居之间的级联时，是否会形成种子邻居的巨大分量；当新激活的节点引发普通节点之间的进一步激活时，是否会维持普通节点的巨大分量。两种巨型成分的共存表明，临界点可能会出现两次。我们举例说明，当种子邻居和普通节点的采用阈值之间存在差距时，就会出现两个临界点。蒙特卡洛模拟清楚地表明，在小临界点出现的第一次级联发生在种子邻居的巨大分量中，而在较大临界点出现的第二次级联延伸到普通节点的巨大分量中。

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引用次数: 0

On the kinetic temperature of a one-dimensional crystal on the long-time scale 论一维晶体的长时间尺度动力学温度

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-09-24 DOI: 10.1016/j.physa.2024.130114

We investigate the dynamics of the kinetic temperature of a finite one-dimensional harmonic chain, the evolution of which is initiated by a thermal shock. We demonstrate that the kinetic temperature returns arbitrarily close to its initial state (the one immediately following the thermal shock) infinitely many times, and we give an estimate for the time elapsed until the recurrence. This assertion is closely related to the Poincare recurrence theorem and we discuss their relation. To estimate the recurrence time we use its averaging along system’s trajectory and provide a rigorous mathematical definition of the mean recurrence time. It turns out that the mean recurrence time exponentially increases with the number of particles in the chain. A connection is established between this problem and the local theorems of large deviations theory.

Previous studies have shown that in such a one-dimensional harmonic chain, at times of order

N

, a thermal echo phenomenon is observed — a sharp increase in the amplitude of kinetic temperature fluctuations. In the present work, we give a rigorous mathematical formulation to this phenomenon and estimate the amplitude of the fluctuations.

The research is funded by the Ministry of Science and Higher Education of the Russian Federation within the framework of the Research Center of World-Class Program: Advanced Digital Technologies (agreement №075-15-2020-311 dated 04.20.2022).

我们研究了由热冲击引发的有限一维谐波链动力学温度的演变。我们证明了动能温度会无限多次地返回到其初始状态（紧随热冲击之后的状态），并给出了直到复现所需的时间估计值。这一论断与庞加莱复现定理密切相关，我们将讨论它们之间的关系。为了估算复发时间，我们采用了沿系统轨迹平均复发时间的方法，并给出了平均复发时间的严格数学定义。结果发现，平均递推时间随链中粒子数量的增加而呈指数增长。以前的研究表明，在这样的一维谐波链中，当时间为 N 阶时，会出现热回声现象--动能温度波动幅度急剧增加。本研究由俄罗斯联邦科学和高等教育部在世界一流计划研究中心框架内资助：该研究由俄罗斯联邦科学和高等教育部在 "世界一流计划：先进数字技术 "研究中心框架内提供资助（协议编号：075-15-2020-311，日期：04.20.2022）。

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引用次数: 0

On the calibration and improvement of human mobility models in intercity transportation system 论城际交通系统中人员流动模型的校准与改进

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-09-23 DOI: 10.1016/j.physa.2024.130116

As one of the research highlights of physics-level modelling, human mobility has generated numerous universal laws over the past decades. However, the majority of research has concentrated on the intracity networks, leaving the intercity mobility systems with insufficient attention despite its increasingly crucial role in the development of urban agglomerations. Related research gaps further extend to the limited understanding of spatiotemporal heterogeneity in intercity human mobility. To bridge these gaps, our study systematically validated and improved the modelling framework of intercity mobility flows utilizing real-world data sources. Specifically, building upon the nationwide Location-based Services (LBS) datasets in China, the applicability of classic human mobility models, including gravity model and intervening opportunities-class models, was extensively explored in the intercity domain by developing fitting models that incorporated multi-class urban attributes. Then, we contributed to proposing improved models that consider the diverse attraction effect of the origin and potential destinations. Moreover, our research scope was expanded to incorporate spatiotemporal heterogeneity through model comparisons among various city sets during both regular period and holiday. The findings suggested that our improved models effectively enhance the modelling accuracy while strengthening the explanatory power. They especially demonstrate a balanced performance even when handling datasets with spatiotemporal heterogeneity. Consequently, this study provides valuable insights into understanding intercity human mobility from the intrinsic mechanism of opportunity attraction. Our models hold practical significance in accurately modelling intercity mobility flows utilizing observable urban attributes and spatial layouts, further providing effective tools for preemptive traffic management.

作为物理学建模的研究重点之一，人类流动性在过去几十年中产生了许多普遍规律。然而，大多数研究都集中在城内网络上，对城际流动系统的关注不够，尽管它在城市群的发展中扮演着越来越重要的角色。相关的研究空白进一步扩展到对城际间人员流动的时空异质性的有限理解。为了弥补这些差距，我们的研究利用现实世界的数据来源，系统地验证并改进了城际流动的建模框架。具体而言，我们以中国全国范围内的位置服务（LBS）数据集为基础，通过开发包含多类城市属性的拟合模型，广泛探讨了重力模型和干预机会类模型等经典人员流动模型在城际领域的适用性。随后，我们提出了考虑出发地和潜在目的地不同吸引效应的改进模型。此外，我们还扩大了研究范围，通过在平时和节假日期间对不同城市集进行模型比较，纳入了时空异质性。研究结果表明，我们改进的模型有效地提高了建模精度，同时增强了解释力。尤其是在处理具有时空异质性的数据集时，这些模型表现出了均衡的性能。因此，本研究为从机会吸引的内在机制来理解城市间人口流动提供了宝贵的见解。我们的模型在利用可观测的城市属性和空间布局对城际流动进行精确建模方面具有重要的现实意义，并进一步为先发制人的交通管理提供了有效工具。

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引用次数: 0