Controllability Of Complex Networks Research Articles

Round Robin-Based Synchronization Control for Discrete-Time Complex Networks With Probabilistic Coupling Delay and Deception Attacks

Round Robin-Based Synchronization Control for Discrete-Time Complex Networks With Probabilistic Coupling Delay and Deception Attacks

Synchronization Mechanism for Controlled Complex Networks under Auxiliary Effect of Dynamic Edges

The scope of complex dynamical networks (CDNs) with dynamic edges is very wide, as it is composed of a class of realistic networks including web-winding systems, communication networks, neural networks, etc. However, a classic research topic in CDNs, the synchronization control problem, has not been effectively solved for CDNs with dynamic edges. This paper will investigate the emergence mechanism of synchronization from the perspective of large-scale systems. Firstly, a CDN with dynamic edges is conceptualized as an interconnected coupled system composed of an edge subsystem (ES) and a node subsystem (NS). Then, based on the proposed improved directed matrix ES model and expanded matrix inequality, this paper overcomes the limitations of coupling term design in node models and the strong correlation of tracking targets between nodes and edges. Due to the effect of the synthesized node controller and the auxiliary effect of the ES, state synchronization can be realized in the CDN. Finally, through simulation examples, the validity and advantages of our work compared to existing methods are demonstrated.

Private Synchronization Control of Complex Networks Against Eavesdropping Attacks: A Differential Privacy Scheme

Private Synchronization Control of Complex Networks Against Eavesdropping Attacks: A Differential Privacy Scheme

Sufficient control of complex networks

In this paper, we propose to study sufficient control of complex networks, which is to control a sufficiently large portion of the network, where only the quantity of controllable nodes matters. To the best of our knowledge, this is the first time that such a problem is investigated. We prove that the sufficient controllability problem can be converted into a minimum-cost flow problem, for which an algorithm with polynomial complexity can be devised. Further, we study the problem of minimum-cost sufficient control, which is to drive a sufficiently large subset of the network nodes to any predefined state with the minimum cost using a given number of controllers. The problem is NP-hard. We propose an “extended L0-norm-constraint-based Projected Gradient Method” (eLPGM) algorithm, which achieves suboptimal solutions for the problems at small or medium sizes. To tackle the large-scale problems, we propose to convert the control problem into a graph problem and devise an efficient low-complexity “Evenly Divided Control Paths” (EDCP) algorithm to tackle the graph problem. Simulation results on both synthetic and real-life networks are provided, demonstrating the satisfactory performance of the proposed methods.

Links synchronization control for complex networks with stochastic links dynamics

Links synchronization control for complex networks with stochastic links dynamics

Evaluation of key node groups of embodied carbon emission transfer network in China based on complex network control theory

China has committed to enhancing its national efforts to peak CO2 emissions by 2030. Rather than applying broad emission reduction measures nationwide, a more efficient strategy involves focusing on select provinces and sectors as pioneers in emission reduction. This targeted approach is expected to drive broader, nationwide progress towards the carbon emission reduction goal, thereby improving the effectiveness of emission reduction policies. This study integrates complex network control theory with input–output modeling to develop a detailed analysis of the embodied carbon emission (EC) transfer network at the province-sector level in China for the year 2017. By examining the network’s characteristics and identifying key nodes for control, the study arrives at several notable findings: (1) EC transfers predominantly flow from central and northern provinces to the economically vibrant southeast provinces, and from energy-intensive sectors towards services, manufacturing, and others. (2) The network’s structure follows a scale-free distribution, indicating that a limited number of provincial sectors are central to the network, managing a majority of EC transfers and thus playing a crucial role. (3) Utilizing complex network control theory, an algorithm was developed to identify optimal control nodes, aiming to minimize the costs associated with emission reduction. This approach identified 287 provincial sectors, about one-third of the total, while keeping abatement costs below one-fourth of potential expenses. (4) The identified crucial control nodes consist of approximately 38% source nodes and 20% isolated nodes. Sectors downstream of these source nodes, such as sec04, sec05, and sec22, show significant node strength and betweenness centrality, enhancing the spread of emission reduction impacts. (5) When compared to other node selection strategies, the algorithm not only achieves complete network control but also minimizes abatement costs effectively. This research offers valuable insights for policymakers on the dynamics of interregional trade and EC management. It supports the formulation of a strategic, long-term plan for collaborative emission reduction across provinces and sectors, aligning with China’s environmental commitments.

Data-driven control of oscillator networks with population-level measurement.

Controlling complex networks of nonlinear limit-cycle oscillators is an important problem pertinent to various applications in engineering and natural sciences. While in recent years the control of oscillator populations with comprehensive biophysical models or simplified models, e.g., phase models, has seen notable advances, learning appropriate controls directly from data without prior model assumptions or pre-existing data remains a challenging and less developed area of research. In this paper, we address this problem by leveraging the network's current dynamics to iteratively learn an appropriate control online without constructing a global model of the system. We illustrate through a range of numerical simulations that the proposed technique can effectively regulate synchrony in various oscillator networks after a small number of trials using only one input and one noisy population-level output measurement. We provide a theoretical analysis of our approach, illustrate its robustness to system variations, and compare its performance with existing model-based and data-driven approaches.

Measuring criticality in control of complex biological networks

Recent controllability analyses have demonstrated that driver nodes tend to be associated to genes related to important biological functions as well as human diseases. While researchers have focused on identifying critical nodes, intermittent nodes have received much less attention. Here, we propose a new efficient algorithm based on the Hamming distance for computing the importance of intermittent nodes using a Minimum Dominating Set (MDS)-based control model. We refer to this metric as criticality. The application of the proposed algorithm to compute criticality under the MDS control framework allows us to unveil the biological importance and roles of the intermittent nodes in different network systems, from cellular level such as signaling pathways and cell-cell interactions such as cytokine networks, to the complete nervous system of the nematode worm C. elegans. Taken together, the developed computational tools may open new avenues for investigating the role of intermittent nodes in many biological systems of interest in the context of network control.

Controllability of Fractional Complex Networks

Controllability is a fundamental issue in the field of fractional complex network control, yet it has not received adequate attention in the past. This paper is dedicated to exploring the controllability of complex networks involving the Caputo fractional derivative. By utilizing the Cayley–Hamilton theorem and Laplace transformation, a concise proof is given to determine the controllability of linear fractional complex networks. Subsequently, leveraging the Schauder Fixed-Point theorem, controllability Gramian matrix, and fractional calculus theory, we derive controllability conditions for nonlinear fractional complex networks with a weighted adjacency matrix and Laplacian matrix, respectively. Finally, a numerical method for the controllability of fractional complex networks is obtained using Matlab (2021a)/Simulink (2021a). Three examples are provided to illustrate the theoretical results.

Time-delay signature suppression in delayed-feedback semiconductor lasers as a paradigm for feedback control in complex physiological networks.

Physiological networks, as observed in the human organism, involve multi-component systems with feedback loops that contribute to self-regulation. Physiological phenomena accompanied by time-delay effects may lead to oscillatory and even chaotic dynamics in their behaviors. Analogous dynamics are found in semiconductor lasers subjected to delayed optical feedback, where the dynamics typically include a time-delay signature. In many applications of semiconductor lasers, the suppression of the time-delay signature is essential, and hence several approaches have been adopted for that purpose. In this paper, experimental results are presented wherein photonic filters utilized in order to suppress time-delay signatures in semiconductor lasers subjected to delayed optical feedback effects. Two types of semiconductor lasers are used: discrete-mode semiconductor lasers and vertical-cavity surface-emitting lasers (VCSELs). It is shown that with the use of photonic filters, a complete suppression of the time-delay signature may be affected in discrete-mode semiconductor lasers, but a remnant of the signature persists in VCSELs. These results contribute to the broader understanding of time-delay effects in complex systems. The exploration of photonic filters as a means to suppress time-delay signatures opens avenues for potential applications in diverse fields, extending the interdisciplinary nature of this study.

Retracted: A Novel Distributed Denial of Service (DDoS) Detection Method in Software Defined Networks

The software Defined Networks (SDN) are the most promising solutions in solving challenges regarding complex network control. The SDNs are responsible for separating network control functions from the network switch. By applying SDNs, communication companies and operators can control and manage networks through one central controller unit therein with no need by applying hardware or the manufacture's guideline. Lack of complex DDoS attack detection, lack of rapid detection sensitivity on packet type attack, lack of methods in promoting detection attack to specific unknown protocols, lack of ability in detecting at switch level, lack of assessing attacks packet types, constitute drawbacks. Although is made in this article to overcome these drawbacks, consequently the GainRatioAttributeEval algorithm and Ranker method are applied. The datasets involved here are the ISCX-SlowDDoS2016, ISCX-IDS2012, CTU-10 and CTU-11.This proposed method is compared with similar methods and it is found that this method with 99.95% accuracy and 0.04 FPR for UNB-ISCX dataset and 99.84% accuracy and 0.1 FPR for CTU-13 dataset is better than others.

Event-Triggered Impulsive Control for Complex Networks Under Stochastic Deception Attacks

Event-Triggered Impulsive Control for Complex Networks Under Stochastic Deception Attacks

Finite-Time Control of Complex Networks With Nondifferential Delays by Bounded Control Laws

Finite-Time Control of Complex Networks With Nondifferential Delays by Bounded Control Laws

Disturbance observer‐based PID synchronization control of complex networks with probabilistic interval time‐varying delays and multiple disturbances

AbstractIn this article, the disturbance observer‐based proportional–integral–derivative (PID) control protocol is investigated for synchronization of complex networks with probabilistic interval time‐varying delays and multiple disturbances. For the sake of reducing conservatism, a method of probabilistic interval time‐varying delay is proposed by introducing two certain intervals with known probability distributions. Moreover, two different disturbances are taken into account. One of the disturbances is produced by an exogenous system which acts through the input channel, while the other is usual norm‐bounded disturbance. Simultaneously, a disturbance observer is designed to estimate and compensate the disturbance generated by the exogenous system. By constructing an appropriate Lyapunov–Krasovskii function, a sufficient criterion is obtained to ensure both the exponential synchronization and prescribed performance index. Finally, the simulation examples are employed to demonstrate the validity of the theoretical results.

Stabilization and synchronization control for discrete-time complex networks via the auxiliary role of edges subsystem

In this paper, a novel discrete-time interconnected model composed of nodes and edges subsystems is proposed, to depict the complex dynamical networks (CDNs) consisted of nodes and dynamic edges which are coupled mutually. Firstly, we propose a vector model to describe the dynamic characteristic of edges rather than the matrix form, the proposed discrete-time CDNs have dynamics attached on not only the nodes but also the edges. Then, to reveal the essential mechanism between network structure and stabilization (synchronization), the control strategies and the coupling modes between two subsystems are designed such that the stabilization and synchronization can be achieved. Finally, a simulation example is given to prove the effectiveness of the proposed methods.

Controllability evaluation of complex networks in cyber–physical power systems via critical nodes and edges

Certain edge failures in cyber–physical power systems (CPPSs) can cause cascading failures in the power system. Protecting these critical edges and essential equipment in the supporting communication network can avoid these failures. Here, based on complex network controllability regarding kinetic equations and considering the actual operating conditions of CPPSs, we filter out a class of transmission lines and a class of nodes in the power system communication network of the power system that show a significant impact on network controllability. We further validate the results of the filtering by examining the resulting network robustness. Through simulations, we observe that the power edges and communication nodes screened based on network controllability have the most significant impacts on system controllability. It is therefore crucial to protect these edges and nodes to achieve more stable power grid operation and prevent cascading failures. Finally, we validate our results using a case study on a city in Henan Province, China. Our simulations demonstrate that the addition of a communication system can enhance the controllability of the power system.

Designing networks with specific synchronization transitions independent of the system’s dynamics

Synchronization transition, a phenomenon widely observed in various networks, can be classified into two types: explosive and continuous transitions. While the continuous transition is relatively well-understood, the underlying mechanisms of the explosive transition present a challenge to researchers. Most studies, however, pointed out the interaction between node dynamics and network topology as the fundamental feature of explosive synchronization in networks. In this study, we demonstrate the impact of Laplacian eigenvalue on the type of synchronization transition observed in complex systems. Through simulations, our results show that the sparsity of the Laplacian eigenvalues is the key to designing the synchronization transitions. The findings reveal that the sparse distributions lead to a jump in the evolution of the order parameter, explosive synchronization. Additionally, the nodes exhibit continuous synchronization in cases where the eigenvalues are broadly dispersed. This study examines three distinct chaotic systems – Chen, Rössler, and Lorenz – with five different Laplacian eigenvalue distributions. Surprisingly, the outcomes demonstrate that the synchronization transition is not mainly dependent on the internal dynamics of the nodes, highlighting a pivotal characteristic that can be used to design the precise control of complex networks.

Dynamic Periodic Event-Triggered Synchronization of Complex Networks: The Discrete-Time Scenario.

This article reports the synchronization control of discrete-time complex networks using an event-triggered method. The main contributions are twofold: 1) a discrete-time scenario of the dynamic periodic event-triggered mechanism is developed to schedule the transmissions of measurements. The proposed mechanism monitors the synchronization error in a periodic manner, which is beneficial to reduce the calculation resources of sensors. Simultaneously, the proposed mechanism increases the triggering threshold so that it contributes to enlarging the average interevent interval and 2) a new Lyapunov functional is developed to deal with the periodic samplings. On the one hand, the proposed functional involves a delay-dependent term, which is convenient to formulate the synchronization criterion by the delay analysis technique. On the other hand, the functional takes the sawtooth constraint of periodic samplings into consideration by introducing a piecewise functional. Finally, a succinct criterion is derived such that the considered networks are synchronized with a predetermined error level. A simulation example is provided to show our advantages in comparison with the existing approaches.

Focus on Monitoring and Control of Complex Supply Systems

Abstract The ongoing rapid transformation of our energy supply challenges the operation and stability of electric power grids and other supply networks. This focus issue comprises new ideas and concepts in the monitoring and control of complex networks to address these challenges.

Coupling Dependence on Chaos Synchronization Process in a Network of Rulkov Neurons

This paper investigates the impact of network conformation on chaos synchronization in driven complex networks in a master-slave setting. We analyze the control and prediction of complex networks under perturbations. Although chaotic dynamics suppression is well understood in low-dimensional systems, it is not well described in more complex ones. To assess a network’s ability to amplify or suppress disturbances, we track ensembles of possible trajectories in the driven subsystem which give insights into nonlocal convergence properties in a master-slave setting. We demonstrate how the stability of forced networks of Rulkov neurons is dependent on network coupling architecture and how the duration of chaotic transients depends on initial conditions. Furthermore, we find that each network generator algorithm has specific sets of parameters that can lead to stable or nonstable states. Our results indicate that network types are more critical than isolated connections for suppressing chaotic dynamics in driven networks. Finally, we identify that under certain circumstances, the transient period before synchronization correlates with high convergence rates for certain initial conditions. This suggests nonuniform convergence rates in the phase space of driven neural networks.