Chaos

Event detection is one of the most important areas of complex network research. It aims to identify abnormal points in time corresponding to social events. Traditional methods of event detection, based on first-order network models, are poor at describing the multivariate sequential interactions of components in complex systems and at accurately identifying anomalies in temporal social networks. In this article, we propose two valid approaches, based on a higher-order network model, namely, the recovery higher-order network algorithm and the innovation higher-order network algorithm, to help with event detection in temporal social networks. Given binary sequential data, we take advantage of chronological order to recover the multivariate sequential data first. Meanwhile, we develop new multivariate sequential data using logical sequence. Through the efficient modeling of multivariate sequential data using a higher-order network model, some common multivariate interaction patterns are obtained, which are used to determine the anomaly degree of a social event. Experiments in temporal social networks demonstrate the significant performance of our methods finally. We believe that our methods could provide a new perspective on the interplay between event detection and the application of higher-order network models to temporal networks.

Xiang Li

Xue Zhang

Qizi Huangpeng

Chengli Zhao

Xiaojun Duan

Chaos An Interdisciplinary Journal of Nonlinear Science

Event detection in temporal social networks using a higher-order network model

We demonstrate for a photonic nonlinear system that two highly asymmetric feedback delays can induce a variety of emergent patterns which are highly robust during the system's global evolution. Explicitly, two-dimensional chimeras and dissipative solitons become visible upon a space-time transformation. Switching between chimeras and dissipative solitons requires only adjusting two system parameters, demonstrating self-organization exclusively based on the system's dynamical properties. Experiments were performed using a tunable semiconductor laser's transmission through a Fabry-Pérot resonator resulting in an Airy function as nonlinearity. Resulting dynamics were bandpass filtered and propagated along two feedback paths whose time delays differ by two orders of magnitude. An excellent agreement between experimental results and the theoretical model given by modified Ikeda equations was achieved.

D. Brunner

B. Penkovsky

R. Levchenko

E. Schöll

L. Larger

Y. Maistrenko

Two-dimensional spatiotemporal complexity in dual-delayed nonlinear feedback systems: Chimeras and dissipative solitons

双延迟非线性反馈系统中的二维时空复杂性：嵌合体与耗散孤子

The complex phase interactions of the two-phase flow are a key factor in understanding the flow pattern evolutional mechanisms, yet these complex flow behaviors have not been well understood. In this paper, we employ a series of gas-liquid two-phase flow multivariate fluctuation signals as observations and propose a novel interconnected ordinal pattern network to investigate the spatial coupling behaviors of the gas-liquid two-phase flow patterns. In addition, we use two network indices, which are the global subnetwork mutual information (I) and the global subnetwork clustering coefficient (C), to quantitatively measure the spatial coupling strength of different gas-liquid flow patterns. The gas-liquid two-phase flow pattern evolutionary behaviors are further characterized by calculating the two proposed coupling indices under different flow conditions. The proposed interconnected ordinal pattern network provides a novel tool for a deeper understanding of the evolutional mechanisms of the multi-phase flow system, and it can also be used to investigate the coupling behaviors of other complex systems with multiple observations.

Meng Du

Jie Wei

Meng-Yu Li

Zhong-ke Gao

Jürgen Kurths

Interconnected ordinal pattern complex network for characterizing the spatial coupling behavior of gas–liquid two-phase flow

用于表征气液两相流空间耦合行为的互联序数模式复杂网络

We review and test twelve different approaches to the detection of finite-time coherent material structures in two-dimensional, temporally aperiodic flows. We consider both mathematical methods and diagnostic scalar fields, comparing their performance on three benchmark examples: the quasiperiodically forced Bickley jet, a two-dimensional turbulence simulation, and an observational wind velocity field from Jupiter's atmosphere. A close inspection of the results reveals that the various methods often produce very different predictions for coherent structures, once they are evaluated beyond heuristic visual assessment. As we find by passive advection of the coherent set candidates, false positives and negatives can be produced even by some of the mathematically justified methods due to the ineffectiveness of their underlying coherence principles in certain flow configurations. We summarize the inferred strengths and weaknesses of each method, and make general recommendations for minimal self-consistency requirements that any Lagrangian coherence detection technique should satisfy.

Alireza Hadjighasem

Mohammad Farazmand

Daniel Blazevski

Gary Froyland

George Haller

A critical comparison of Lagrangian methods for coherent structure detection

拉格朗日方法在相干结构检测中的关键比较

In this paper, we propose a novel control approach for opinion dynamics on evolving networks. The controls modify the strength of connections in the network, rather than influencing opinions directly, with the overall goal of steering the population toward a target opinion. This requires that the social network remains sufficiently connected, the population does not break into separate opinion clusters, and that the target opinion remains accessible. We present several approaches to address these challenges, considering questions of controllability, instantaneous control, and optimal control. Each of these approaches provides a different view on the complex relationship between opinion and network dynamics and raises interesting questions for future research.

A. Nugent

S. N. Gomes

M. T. Wolfram

Steering opinion dynamics through control of social networks

通过控制社交网络引导舆论动态

Complex networks are susceptible to contagious cascades, underscoring the urgency for effective epidemic mitigation strategies. While physical quarantine is a proven mitigation measure for mitigation, it can lead to substantial economic repercussions if not managed properly. This study presents an innovative approach to selecting quarantine targets within complex networks, aiming for an efficient and economic epidemic response. We model the epidemic spread in complex networks as a Markov chain, accounting for stochastic state transitions and node quarantines. We then leverage deep reinforcement learning (DRL) to design a quarantine strategy that minimizes both infection rates and quarantine costs through a sequence of strategic node quarantines. Our DRL agent is specifically trained with the proximal policy optimization algorithm to optimize these dual objectives. Through simulations in both synthetic small-world and real-world community networks, we demonstrate the efficacy of our strategy in controlling epidemics. Notably, we observe a non-linear pattern in the mitigation effect as the daily maximum quarantine scale increases: the mitigation rate is most pronounced at first but plateaus after reaching a critical threshold. This insight is crucial for setting the most effective epidemic mitigation parameters.

Jie Yang

Wenshuang Liu

Xi Zhang

Choujun Zhan

Mitigating epidemic spread in complex networks based on deep reinforcement learning

基于深度强化学习的复杂网络中的流行病传播缓解

Given two unidirectionally coupled nonlinear systems, we speak of generalized synchronization when the responder “follows” the driver. Mathematically, this situation is implemented by a map from the driver state space to the responder state space termed the synchronization map. In nonlinear times series analysis, the framework of the present work, the existence of the synchronization map amounts to the invertibility of the so-called cross map, which is a continuous map that exists in the reconstructed state spaces for typical time-delay embeddings. The cross map plays a central role in some techniques to detect functional dependencies between time series. In this paper, we study the changes in the “noiseless scenario” just described when noise is present in the driver, a more realistic situation that we call the “noisy scenario.” Noise will be modeled using a family of driving dynamics indexed by a finite number of parameters, which is sufficiently general for practical purposes. In this approach, it turns out that the cross and synchronization maps can be extended to the noisy scenario as families of maps that depend on the noise parameters, and only for “generic” driver states in the case of the cross map. To reveal generalized synchronization in both the noiseless and noisy scenarios, we check the existence of synchronization maps of higher periods (introduced in this paper) using recurrent neural networks and predictability. The results obtained with synthetic and real-world data demonstrate the capability of our method.

José M. Amigó

Roberto Dale

Juan C. King

Klaus Lehnertz

Generalized synchronization in the presence of dynamical noise and its detection via recurrent neural networks

存在动态噪声时的广义同步及其通过递归神经网络的检测

We investigate how the magnetic structures of the plasma change in a large aspect ratio tokamak perturbed by an ergodic magnetic limiter, when a system parameter is non-adiabatically varied in time. We model such a scenario by considering the Ullmann–Caldas nontwist map, where we introduce an explicit time-dependence to the ratio of the limiter and plasma currents. We apply the tools developed recently in the field of chaotic Hamiltonian systems subjected to parameter drift. Namely, we follow trajectory ensembles initially forming Kolmogorov Arnold Moser (KAM) tori and island chains in the autonomous configuration space. With a varying parameter, these ensembles, called snapshot tori, develop time-dependent shapes. An analysis of the time evolution of the average distance of point pairs in such an ensemble reveals that snapshot tori go through a transition to chaos, with a positive Lyapunov exponent. We find empirical power-law relationships between both the Lyapunov exponent and the beginning of the transition to chaos (the so-called critical instant), as a function of the rate of the parameter drift, with the former showing an increasing trend and the latter a decreasing trend. We conclude that, in general, coherent tori and magnetic islands tend to break up and become chaotic as the perturbation increases, similar to the case of subsequent constant perturbations. However, because of the continuous drift, some structures can persist longer and exist even at perturbation values where they would not be observable in the constant perturbation case.

Dániel Jánosi

Anikó Horváth

Lili Édes

Tamás Kovács

Magnetic structures in the explicitly time-dependent nontwist map

显式时间依赖非扭转映射中的磁结构

The development of robust Early Warning Signals (EWSs) is necessary to quantify the risk of crossing tipping points in the present-day climate change. Classically, EWSs are statistical measures based on time series of climate state variables, without exploiting their spatial distribution. However, spatial information is crucial to identify the starting location of a transition process and can be directly inferred by satellite observations. By using complex networks constructed from several climate variables on the numerical grid of climate simulations, we seek for network properties that can serve as EWSs when approaching a state transition. We show that network indicators such as the normalized degree, the average length distance, and the betweenness centrality are capable of detecting tipping points at the global scale, as obtained by the MIT general circulation model in a coupled-aquaplanet configuration for CO2 concentration-driven simulations. The applicability of such indicators as EWSs is assessed and compared to traditional methods. We also analyze the ability of climate networks to identify nonlinear dynamical patterns.

Laure Moinat

Jérôme Kasparian

Maura Brunetti

Tipping detection using climate networks

利用气候网络进行临界点检测

Macroscopic features of dynamical systems such as almost-invariant sets and coherent sets provide crucial high-level information on how the dynamics organizes phase space. We introduce a method to identify time-parameterized families of almost-invariant sets in time-dependent dynamical systems, as well as the families’ emergence and disappearance. In contrast to coherent sets, which may freely move about in phase space over time, our technique focuses on families of metastable sets that are quasi-stationary in space. Our straightforward approach extends successful transfer operator methods for almost-invariant sets to time-dependent dynamics and utilizes the Ulam scheme for the generator of the transfer operator on a time-expanded domain. The new methodology is illustrated with an idealized fluid flow and with atmospheric velocity data. We identify atmospheric blocking events in the 2003 European heatwave and compare our technique to existing geophysical methods of blocking diagnosis.

Aleksandar Badza

Identifying the onset and decay of quasi-stationary families of almost-invariant sets with an application to atmospheric blocking events

识别准平稳几乎不变集族的起始和衰减及其在大气阻塞事件中的应用

Abbreviation	Chaos
Journal Impact	2.67
Quartiles(Global)	PHYSICS, MATHEMATICAL(Q1)

ISSN	1054-1500, 1089-7682, 1527-2443
h-index	127

Publication Information	Publisher: American Institute of Physics，Publishing cycle: Quarterly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1991，Proportion of original research papers: 98.21%，Self Citation Rate:18.50%， Gold OA Rate: 15.12%
Average review cycle	网友分享经验：平均3.0个月
Average recruitment ratio	网友分享经验：较难

Chaos

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