Complex Network System Research Articles

Pth moment asymptotic stability for stochastic complex networked control systems with Lévy noise

Pth moment asymptotic stability for stochastic complex networked control systems with Lévy noise

Network anomaly detection and security defense technology based on machine learning: A review

Robust solutions are essential for protecting complex network systems in the constantly changing cybersecurity scenario. This investigation examines the role of machine learning (ML) in improving the safety of digital infrastructure by examining network anomaly detection and security defense. We evaluate the effectiveness of key ML techniques, including deep learning (DL), ensemble methods, and supervised and unsupervised learning, in recognizing abnormalities and reducing dangers. Real-time monitoring and adaptive reactions are emphasized in the analysis of ML-based systems integration into comprehensive security frameworks. This section also addresses challenges like interpretability, harmful assaults, and model resilience. Real-world case examples emphasize the importance of ML in the advancement of network security. The paper continues with a discussion of ML's future role in the optimization of security measures and the mitigation of emergent threats.

Mittag-Leffler function based security control for fractional-order complex network system subject to deception attacks via Observer-based AETS and its applications

The goal of this paper is to investigate the security control for uncertain fractional-order delayed complex network systems under deception attacks using the Mittag-Leffler function and observer-based adaptive event-triggered scheme (AETS) with the fractional commensurate order in q ∈ (0, 1). The adaptive event-triggering scheme is used during the data transmission process from the sensors to the observer, where the triggering threshold can be dynamically modified to reduce resource waste. We make a novel model for the estimation error system that takes into account both the effects of the adaptive event-triggered scheme and the effects of deception attacks. A sufficient condition is obtained to guarantee the stochastic mean-square stability of the augmented error system using the Mittag-Leffler (M-L) functions and the Lyapunov functional method and by using the singular value decomposition (SVD) and linear matrix inequality (LMI) techniques, the co-design problem of desired observer and controller gains is found, and it is shown that the solution ensures the stability of a closed-loop uncertain fractional-order complex networked system. At the end of this study, two numerical examples and diesel engine system model are given to show that the above findings are correct.

What are the key factors influencing scientific data sharing? A combined application of grounded theory and fuzzy-DEMATEL approach

Scientific data sharing (SDS) has become essential for scientific progress, technological innovation and socioeconomic development. Identifying the key influencing factors of SDS can effectively promote SDS programmes and give full play to the critical role of scientific data. This study used grounded theory and information ecology theory to construct an SDS influencing factor model that encompassed five dimensions and 28 influencing factors and followed the fuzzy decision-making trial and evaluation laboratory (fuzzy-DEMATEL) approach to measure and analyse the degree of influence of each influencing factor and identify the key factors. The results show that (1) there are interactions and mutual interactions between the various influencing factors of SDS, which can form a complex network system. (2) 16 influencing factors, such as data-sharing policies, data-sharing regulations and data-sharing standards, comprise the key influencing factors in SDS. (3) The optimisation path of SDS is ‘Scientific Researchers’ → ‘Scientific Data’ → ‘Policy Environment’ → ‘Research Organisations → ‘Information Technologies’. In this regard, we proposed the following management suggestions to promote the development of SDS programmes in China: focusing on researchers' subjective willingness to share, enhancing the integrated governance of scientific data, fulfilling the role of policy support and guidance, strengthening the support of research organisations and improving SDS platforms with information technology.

Unraveling the dynamics of social governance innovation: A synergistic approach employing NLP and network analysis

Society, inherently a complex network system, necessitates an equally intricate approach towards social governance. This study aims to dissect the spatio-temporal evolution of social governance innovation themes and explore the value network relationships among multiple stakeholders in social governance. To achieve this, we employ natural language processing to analyze text data from 285 social governance innovation cases. This analysis allows us to delve into the progression of social governance innovation practice themes, discern inherent patterns and modes, and reveal their spatio-temporal characteristics. These insights form the basis for our proposed recommendations on policy-making and public service delivery to address public problems. Concurrently, we utilize the ERNIE-UIE model for transfer learning to extract entities related to social governance stakeholders from the cases. Our findings suggest that governance practices extending beyond the government to include all possible stakeholders, such as enterprises and civil society, are essential. When combined with social network analysis, we can illuminate the value network relationships among these multiple social governance stakeholders surrounding particular innovative practice themes. Our study sheds light on the increasing importance of public–private partnerships and the emergence of social organizations as key agents of influence within various domains of social governance.

Parameter identification method of information propagation models based on different network structures

In this paper, we create the rumor propagation model with diffusion behavior by considering the state of the rumor in both the time dimension and the space dimension comprehensively. Meanwhile, we demonstrate the reaction–diffusion model using Turing patterns after determining the prerequisites for their occurrence. In order to achieve the purpose of predicting and controlling rumors in time, we choose to utilize the parameter identification technique based on the Barzilai–Borwein (BB) algorithm and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. In the numerical simulation section, we first investigate how the rumor avoidance rate and cross-diffusion coefficients affect the propagation of rumors. Then, based on a continuous spatio-temporal system and complex network system, respectively, we perform parameter identification for the propagation model. We thoroughly examine how the type of algorithm, the quantity of unknown parameters, and the network structure affect the identification outcomes in terms of the cost function, error curve, and program function time. When the model constructed in this paper is used for parameter identification on different network structures, the error gap between the final value and the target value is not significant. However, the cost function and time consumption for parameter identification on complex networks are much smaller than on the continuous medium.

Thermal conductivities and complex network properties of fractal self-assembled/self-organized texture in binary composite materials

To investigate the relationship between histological properties and thermal conductivities of binary self-assembled/self-organized fractal materials fabricated by the mixed diffusion of filler particles, the particle group distribution of the filler particles was analyzed. Unlike conventional research on particle size distribution, we performed an analysis that focuses on interactions and connectivity between particles from the perspective of the Barabasi-Albert model. In three complex network systems of β-Si3N4 (SN)/austenitic stainless steel (SUS), SN/piezoelectric polyvinylidene fluoride (PVDF), and BaTiO3 (BT)/PVDF, the size distribution of secondary particles was a power-law distribution until the particle group grew relatively large. In the SN/SUS and SN/PVDF systems, a crossover phenomenon was observed in which the order distribution exhibited a power-law in the region where the particle group area was small, and as the particle group became larger, the order distribution became exponential distribution. On the other hand, the BT/PVDF system deviated from the power law distribution as the particle group area increased but did not exhibit an exponential distribution. The characteristics of the particle group distribution of these filler particles were closely related to thermal conductivity, indicating that the connection of particle groups was important. Furthermore, the relationship between the multifractal capacitance dimension, which is strongly correlated with the distance between the particle groups, and the total number of filler particle groups suggests that the texture of these composite material systems may have both small-world and scale-free properties. We also focused on the relationship between the multifractal and complex network properties of the three particle groups. As a result, a correlation was found between the slope γ, which reflects the characteristics of the power distribution, and the thermodynamic parameter α (corresponding to internal energy change) obtained from multifractals. In particular, the smaller the change in γ relative to the change in α, the more pronounced the bonding of particles and particle groups, while the larger change in γ suggested the dispersion of particle groups.

Percolation behavior analysis on n-layer edge-coupled interdependent networks

The robustness of interdependent networks has always been studied for years and has attracted wide attention from researchers in different fields. Noting that not all networks are coupled by nodes, networks coupled by edge are also worth further studying. At present, most studies on the edge-coupled interdependent networks are built on double layers, however, researches on multi-layer edge-coupled interdependent networks have hardly been conducted. In this paper, an n-layer edge-coupled interdependent network model was proposed, and the cascading failure propagation mechanism caused by attacking a small number of edges was studied. Based on the self-consistent probability theory that can significantly simplify the mathematical analysis of such systems, the phase transition behavior was theoretically analyzed. The research results help to deepen the understanding of the robustness of multi-layer interdependent networks and provide some valuable advice for the protection and improvement of complex network systems.

Evolutionary Digital Twin-Oriented Complex Networked Systems driven by node features and the mutation of feature preferences.

Accurate modelling of complex social systems, where people interact with each other and those interactions change over time, has been a research challenge for many years. This study proposes an evolutionary Digital Twin-Oriented Complex Networked System (DT-CNS) framework that considers heterogeneous node features and changeable connection preferences. We create heterogeneous preference mutation mechanisms to characterise nodes' adaptive decisions on preference mutation in response to interaction patterns and epidemic risks. In this space, we use nodes' interaction utilities to characterise the positive feedback from interactions and negative impact of epidemic risks. We also introduce social capital constraint to harness the density of social connections better. The nodes' heterogeneous preference mutation styles include the (i)inactive style that keeps initial social preferences, (ii) ignorant style that randomly mutates preferences, (iii) egocentric style that optimises individual interaction utility, (iv) cooperative style that optimises the total interaction utilities by group decisions and (v) collaborative style that further allows the cooperative nodes to transfer social capital. Our simulation experiments on evolutionary DT-CNSs reveal that heterogeneous preference mutation styles lead to various interaction and infection patterns. The results also show that (i) increasing social capital enables higher interactions but higher infection risks and uncertainty in decision-making; (ii) group decisions outperform individual decisions by eliminating the unawareness of the decisions of other nodes; (iii) the collaborative nodes under a strict social capital limit can promote interactions, reduce infection risks and achieve higher overall interaction utilities.

Modeling and simulation of xenon storage and supply system for electric propulsion system based on fundamental equation of state

The electric propulsion system (EPS) xenon storage and supply system (SSS) is a complex pipeline network system, which involves the multi-physics coupled fields of transcritical flow, heat transfer, rigid body motion, electromagnetics, and control. Based on the fundamental equation of state, a one-dimensional finite volume model system is proposed for compressible transcritical transient pipe flow. Six typical components of the SSS are modularization modeled by using an object-oriented programming method in Fortran 2018. To evaluate the performance of the algorithm, a simulation model consisting of 77 components is developed for a proportional controlled EPS xenon SSS, and a system-level simulation is performed. Compared with the standard case constructed on the Amesim platform, the model has a pressure rise time error lower than 6%, and the restrictors mass-flow-rate errors of the anode, cathode, and neutralizer are less than 2.8% of the rated flow rate after stabilization. Moreover, the proposed model yields more accurate results at certain operating points. The model system provides an efficient reference for the design and optimization of EPS SSSs, and offers a new approach to simulate complex cryogenic pipe flow systems.

Emergent lifetime distribution from complex network systems aging

Most theoretical analysis for lifetime distribution explains origins of specific distribution based on independent failure. We develop one unified framework encompassing different kinds of lifetime distribution for failure coupling system. For typical complex networks, we found that three types of system lifetime distributions are emerged shaped by system size and failure coupling strength. When the failure coupling strength ϕ dominates, systems exhibit a cascade failure mode, the system lifetime following an exponential distribution as series systems due to long-range correlation. When the system size N dominates, systems exhibit wear-out failure mode, the system lifetime following the Gompertz model as parallel systems due to short-range correlation. When N and ϕ have comparable impact on systems, system lifetime follows a modified Weibull distribution. We find the critical failure coupling strength and critical system size which are helpful to identify the failure mode switch point. We provide rigorous theoretical analysis for emerged lifetime distribution. We reveal the microscopic mechanism of system lifetime distribution switch pattern by analyzing the competence between correlation length and network diameter. Finally, we verify our conclusions in real networks. Our study will help to understand the lifetime origin of complex systems and design highly reliable systems.

Reconstructing the evolution history of networked complex systems

The evolution processes of complex systems carry key information in the systems’ functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks.

Delay-driven phase transitions in an epidemic model on time-varying networks.

A complex networked system typically has a time-varying nature in interactions among its components, which is intrinsically complicated and therefore technically challenging for analysis and control. This paper investigates an epidemic process on a time-varying network with a time delay. First, an averaging theorem is established to approximate the delayed time-varying system using autonomous differential equations for the analysis of system evolution. On this basis, the critical time delay is determined, across which the endemic equilibrium becomes unstable and a phase transition to oscillation in time via Hopf bifurcation will appear. Then, numerical examples are examined, including a periodically time-varying network, a blinking network, and a quasi-periodically time-varying network, which are simulated to verify the theoretical results. Further, it is demonstrated that the existence of time delay can extend the network frequency range to generate Turing patterns, showing a facilitating effect on phase transitions.

Study on precursor features of coal and rock loading failure based on difference network

In order to reveal the spatio-temporal precursor features of coal and rock loading failure, and accurately identify the critical state of coal and rock failure, the development of coal and rock loading failure is regarded as the spatio-temporal evolution dynamical process of the complex network system, the spatio-temporal evolution dynamical characteristics of coal and rock dynamic disasters are analyzed. The precursor features indicators I(t) and Ia(t) of the coal and rock failure based on the difference network are proposed, Brazilian splitting experiments of different types of coal and rock are carried out. Combined with the damage theory, the load model of coal and rock damage characterization based Ia(t) is established. The results show that when the coal-rock system is in the warning critical period, the damage is accelerated, with high potential energy, low rebound and weak robustness, which is reflected in the acoustic emission (AE) and electromagnetic radiation (EMR) data at different spatial monitoring points. When the coal-rock system is in the stable stage, the structure of the correlation network at adjacent moment is similar, and the number of edges in the difference network is small. While when coal or rock is about to fracture, the correlation network at adjacent moment has great structure differences, and there are many edges in the difference network. I(t) and Ia(t) synthesize the signal characteristics of different spatial monitoring points, and reflect the spatio-temporal evolution process and damage state of coal and rock. The damage characterization load based on Ia(t) is in good agreement with the measured load, and the correlation coefficients between the measured load and the calculated load based on Ia(t) of most rock samples are more than 0.80. The research results can be used as the precursor features of coal and rock failure, and have certain significance for the early warning of coal and rock dynamic disasters.

Assessment and solutions for vulnerability of urban rail transit network based on complex network theory: A case study of Chongqing

As a typical complex network system, the operating environment of rail transit network (RTN) is complex and demanding. This study aims to accurate assess the weaknesses and vulnerability of RTN, which is crucial for ensuring its smooth operation. Taking Chongqing Rail Transit (CRT) as an example, this study developed a network topology model using the spatial L method and analyzed the network structure characteristics, along with the importance of key nodes under different indicators, based on complex network theory. Additionally, this study analyzed the geographical spatial distribution characteristics of nodes based on the topography and urban spatial structure of Chongqing. Then, this study classified the nodes in the RTN according to basic topological indicators, namely degree, betweenness centrality, network efficiency, and passenger flow volume (PFV). The results indicated six cluster of nodes, reflecting the variability in node vulnerability concerning overall influence (providing alternative paths, reducing path length), regional aggregation capacity, and transportation capacity. Finally, this study proposed targeted management strategies for different clusters of nodes and their respective geographical locations, providing necessary references for rational planning, safety protection, and sustainable construction of RTN.

Il ruolo dello svuotamento gastrico nell’omeostasi e nella controregolazione glucidica

Maintenance of plasma glucose (PG) homeostasis is accomplished thanks to a complex network system. Even a minor fall in PG may activate multiple neuroendocrine actions promoting hierarchical responses, to prevent and ultimately recover hypoglycemia, primarily neuroglycopenia. Among these responses, gastric emptying (GE) plays an important role by coordinated mechanisms which regulate transit and absorption of nutrients through the small intestine. A bidirectional relationship between GE and glycemia has been established: GE may explain the up to 30-40% variance in glycemic response following a carbohydrate-rich meal. In addition, acute and chronic hyperglycemia induce deceleration of GE after meals. Hypoglycemia accelerates GE, in order to speed up carbohydrate absorption, but its role in counterregulation has been poorly investigated, although it was recognized shortly after the initial use of insulin in human.

Characteristics and controlling factors of tectonic fractures within the buried hill reservoirs from the Archaean metamorphic basement: a case study in the Bozhong 19–6 condensate gas field, Bohai Bay Basin

The Bozhong 19–6 Condensate gas field is the first giant gas field discovered in the Bohai Bay Basin in recent years. The gas field reservoir has diverse reservoir space and strong heterogeneity.In this paper, we systematically summarize tectonic fractures development patterns and explore the main factors controlling fractures development and clarify the influence of fractures on reservoir quality through core and microscopic thin section observations, physical property data, imaging logging data. The results show that the major types of tectonic fractures in study area are shear fractures, followed by tensile fractures. Study area mainly developed high-angle fractures and completely filled fractures, fractures are commonly filled with carbonate and clay cement. Four groups of tectonic fractures were found in the study area, which corresponds to the four fracture formation period. Tectonic movement controls the fractures development in study area, the multi-stage tectonic movement had caused a complex fractures network system. The faults control the development of associated fractures formation in the fractures zone, different faults control the fractures formation and orientations in the vicinity. Rock type and minrals content is the basis of forming different fractures, such as the high felsic content is the basis of developed fractures in metamorphic rocks, and rock layer thickness, reservoir porosity and permeability are important factors of the fractures development heterogeneity longitudinally.

Data Freshness in Information-update Systems: Modeling, Scheduling, and Tradeoffs

Brief Biography: Zhongdong Liu is a final-year Ph.D. student in the Department of Computer Science at Virginia Tech. He received his B.S. degree in Mathematics and Applied Mathematics with honors from Northeast Forestry University in 2016. His research interests are in the modeling, analysis, control, and optimization of complex network systems. His specific area of focus lies in analyzing and improving data freshness within real-time applications and services through the development of efficient scheduling algorithms. Zhongdong's research has been featured in notable publications, including IEEE INFOCOM, Proceedings of ICCCN (as an invited paper), IEEE Internet of Things Journal, and IEEE/ACM Transactions on Networking.

Towards Digital Twin-Oriented Complex Networked Systems: Introducing heterogeneous node features and interaction rules.

This study proposes an extendable modelling framework for Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with a goal of generating networks that faithfully represent real-world social networked systems. Modelling process focuses on (i) features of nodes and (ii) interaction rules for creating connections that are built based on individual node's preferences. We conduct experiments on simulation-based DT-CNSs that incorporate various features and rules about network growth and different transmissibilities related to an epidemic spread on these networks. We present a case study on disaster resilience of social networks given an epidemic outbreak by investigating the infection occurrence within specific time and social distance. The experimental results show how different levels of the structural and dynamics complexities, concerned with feature diversity and flexibility of interaction rules respectively, influence network growth and epidemic spread. The analysis revealed that, to achieve maximum disaster resilience, mitigation policies should be targeted at nodes with preferred features as they have higher infection risks and should be the focus of the epidemic control.

Community Graph Convolution Neural Network for Alzheimer's Disease Classification and Pathogenetic Factors Identification.

As a complex neural network system, the brain regions and genes collaborate to effectively store and transmit information. We abstract the collaboration correlations as the brain region gene community network (BG-CN) and present a new deep learning approach, such as the community graph convolutional neural network (Com-GCN), for investigating the transmission of information within and between communities. The results can be used for diagnosing and extracting causal factors for Alzheimer's disease (AD). First, an affinity aggregation model for BG-CN is developed to describe intercommunity and intracommunity information transmission. Second, we design the Com-GCN architecture with intercommunity convolution and intracommunity convolution operations based on the affinity aggregation model. Through sufficient experimental validation on the AD neuroimaging initiative (ADNI) dataset, the design of Com-GCN matches the physiological mechanism better and improves the interpretability and classification performance. Furthermore, Com-GCN can identify lesioned brain regions and disease-causing genes, which may assist precision medicine and drug design in AD and serve as a valuable reference for other neurological disorders.