Diffusion Models for Information Dissemination Dynamics in Wireless Complex Communication Networks

Community structure is an important feature of complex networks. These community structures have the fractal characteristics, that is, there is a self similarity of statistical sense between the complex networks and their local. There have been more and more recent researches on communities’ discovery in complex network. However, most existing approaches require the complete information of entire network, which is impractical for some networks, e.g. the dynamical network and the network that is too large to get the whole information. Therefore, the study of community discovery in complex networks has rather important theoretical and practical value. Through the analysis and study of the complex network evolution models with renormalization and the community change of the complex network evolution, using the tool of adjusting scales as the renormalization process, a multi-scale network community detection algorithm based on fractal feature evolution was proposed. The purpose is to solve community discovery problems in dynamic complex networks, and the effectiveness of the proposed method is verified by real data sets. By comparing result of this paper with the previous methods on some real world networks, and experimental results verify the feasibility and accuracy.

Complex networks are ubiquitous even in the form of defense networks in many fields such as ecological immune systems, biological immune systems, brain networks, air defense systems, national security, cyber security, peace and conflict studies, and so on. In the real world, complex networks exist in both static and dynamic forms. By understanding probabilistic failures and their possible defense mechanisms in static and dynamic complex networks, scientists and researchers can make decisions in developing and implementing methodologies of many fields as we can say, from the fields of biology to fields of technology. In this research, static and dynamic scale-free and small world networks have been generated as complex networks with network generation algorithms. Besides that, probabilistic failures and their possible defense mechanisms have also been generated and designed for previously generated complex networks. On these generated probabilistic failures and their possible defense mechanism, several mechanistic and statistical analysis methods have been applied. To generate these static and dynamic complex networks and their probabilistic failures and possible defense mechanism, two modified network generation algorithms along with several network measurements algorithms have been implemented where parameters have been chosen to produce possible types of static and dynamic scale-free and small world networks, and their corresponding probabilistic failures and possible defense mechanisms. Besides analyzing probabilistic failures and possible defense mechanisms for generated complex networks; the robustness, stability and sustainability of those networks have been studied and analyzed for probabilistic failures and their possible defense mechanisms.

Complex systems provide an opportunity to analyze the essence of phenomena by studying their intricate connections. The networks formed by these connections, known as complex networks, embody the underlying principles governing the system’s behavior. While complex networks have been previously applied in the field of evolutionary computation, prior studies have been limited in their ability to reach conclusive conclusions. Based on our investigations, we are against the notion that there is a direct link between the complex network structure of an algorithm and its performance, and we demonstrate this experimentally. In this paper, we address these limitations by analyzing the dynamic complex network structures of five algorithms across three different problems. By incorporating mathematical distributions utilized in prior research, we not only generate novel insights but also refine and challenge previous conclusions. Specifically, we introduce the biased Poisson distribution to describe the algorithm’s exploration capability and the biased power-law distribution to represent its exploitation potential during the convergence process. Our aim is to redirect research on the interplay between complex networks and evolutionary computation towards dynamic network structures, elucidating the essence of exploitation and exploration in the black-box optimization process of evolutionary algorithms via dynamic complex networks.

Finding the global minimum in complex networks while avoiding local minima is challenging in many types of networks. In human networks and communities, adapting and finding new stable states amid changing conditions due to conflicts, climate changes, or disasters, is crucial. We studied the dynamics of complex networks of violin players and observed that such human networks have different methods to avoid local minima than other non-human networks. Humans can change the coupling strength between them or change their tempo. This leads to different dynamics than other networks and makes human networks more robust and better resilient against perturbations. We observed high-order vortex states, oscillation death, and amplitude death, due to the unique dynamics of the network. This research may have implications in politics, economics, pandemic control, decision-making, and predicting the dynamics of networks with artificial intelligence.

The robustness of controlling complex networks is significant in network science. In this paper, we focus on evaluating and analyzing the robustness of controlling edge dynamics in complex networks against node failure. Using three categories of all nodes to quantify the robustness, we find that the percentages of the three types of nodes are mainly related to the degree distribution of networks. The simulation results of model networks and analytic calculations show that the sparse inhomogeneous networks, which emerge in many real complex networks, have strong control robustness from the point of the number of ordinary nodes, but the strong positive correlation between in and out degrees reduces the control robustness. Evaluation of real-world networks indicates that most of them have few or no critical nodes, that is, they do not need to increase driver nodes to maintain control for most of node failures. Then an adding circuit-link strategy is proposed to optimize the robustness of edge controllability.

This paper is devoted to the study of synchronization problems in uncertain dynamical networks with time-delayed nodes and coupling delays. First, a complex dynamical network model with time-delayed nodes and coupling delays is given. Second, for a complex dynamical network with known or unknown but bounded nonlinear couplings, an adaptive controller is designed, which can ensure that the state of a dynamical network asymptotically synchronizes at the individual node state locally or globally in an arbitrary specified network. Then, the Lyapunov-Krasovskii stability theory is employed to estimate the network coupling parameters. The main results provide sufficient conditions for synchronization under local or global circumstances, respectively. Finally, two typical examples are given, using the M-G system as the nodes of the ring dynamical network and second-order nodes in the dynamical network with time-varying communication delays and switching communication topologies, which illustrate the effectiveness of the proposed controller design methods.

Complex networks have established themselves in recent years as being particularly suitable and flexible for representing and modelling several complex natural and artificial systems. In the same time in which the structural intricacies of such networks are being revealed and understood, efforts have also been directed at investigating how such connectivity properties define and constrain the dynamics of systems unfolding on such structures. However, less attention has been focused on hybrid systems, i.e. involving more than one type of network and/or dynamics. Several real systems present such an organization, e.g. the dynamics of a disease coexisting with the dynamics of the immune system. The current paper investigates a specific system involving diffusive (linear and nonlinear) dynamics taking place in a regular network while interacting with a complex network of defensive agents following Erdös–Rényi (ER) and Barabási–Albert (BA) graph models with moveable nodes. More specifically, the complex network is expected to control, and if possible, to extinguish the diffusion of some given unwanted process (e.g. fire, oil spilling, pest dissemination, and virus or bacteria reproduction during an infection). Two types of pattern evolution are considered: Fick and Gray–Scott. The nodes of the defensive network then interact with the diffusing patterns and communicate between themselves in order to control the diffusion. The main findings include the identification of higher efficiency for the BA control networks and the presence of relapses in the case of the ER model.

Voter model dynamics in complex networks are vulnerable to adversarial attacks. In particular, the voting outcome can be inverted by adding extremely small perturbations that are strategically generated in social networks, even when one opinion is dominant over the other. However, the mitigation of adversarial attacks on the voter model dynamics in complex networks has not been thoroughly investigated. Thus, we examined network structures that could mitigate adversarial attacks using model networks and real-world networks, considering that the network structure affects the voter model dynamics. Numerical simulations demonstrated that the heterogeneity of node degrees in the networks (degree heterogeneity) significantly mitigates adversarial attacks. In particular, for complex networks with a power-law degree distribution , the mitigation effect is significant for . However, the mitigation effect of the degree heterogeneity was relatively weak for large and dense networks. The degree correlation and clustering in the networks exhibited almost no mitigation effect. The results enhance our understanding of how opinion dynamics and collective decision-making are distorted in social networks and may be useful for considering defense strategies against adversarial attacks.

Complex network analysis which can be represented as graph has gained much interest from researchers recently. Analysis derived from complex network leading to a discovery of important group or community lies within the network. It imposes a significant challenge to computer scientists, physicists, and sociologists alike, to identify and discover the true meaning of community for complex network. Different community detection algorithms have been proposed in different perspective of almost similar aim of identifying the community. In this paper, we apply the modularity measurement on complex network and test the strengthness of community found by algorithm proposed. The main study focuses on the importance of having robust algorithm in detecting communities in different type of complex network. Experimental results show that the method is able to successfully separate community by achieving an ideal modularity value.

Dynamic network link prediction is a hot research problem in complex networks. Network representation learning can effectively learn the similarity of nodes and can be used for link prediction. Existing dynamic network representation learning methods mainly discrete window dynamic networks and then model them on static network snapshot graphs, which is difficult to effectively deal with dynamic networks with fine-grained temporal characteristics. In this paper, we propose a link prediction model that can learn complex temporal properties in dynamic networks. The model uses continuous time event sequences to represent dynamic networks, learns continuous temporal information and structural evolution features in the networks, and proposes a temporal attention-based information transfer mechanism to model the diffusion and aggregation of information in the networks. Finally, it transforms link prediction into a classification problem. The experiments are conducted on four real dynamic network datasets and simulated networks, using ap and auc as evaluation metrics. The experimental results of real networks demonstrate that the model can effectively learn the continuity of network evolution and obtain an effective node representation, thus improving the link prediction effect. The experimental results of the simulated network show that the effect of link prediction is related to the network model, but the model in this paper can still obtain better prediction results.

Intrinsic properties of Boolean dynamics in complex networks

The identification of flow pattern is a basic and important issue in multiphase systems. Because of the complexity of phase interaction in gas-liquid two-phase flow, it is difficult to discern its flow pattern objectively. In this paper, we make a systematic study on the vertical upward gas-liquid two-phase flow using complex network. Three unique network construction methods are proposed to build three types of networks, i.e., flow pattern complex network (FPCN), fluid dynamic complex network (FDCN), and fluid structure complex network (FSCN). Through detecting the community structure of FPCN by the community-detection algorithm based on K -mean clustering, useful and interesting results are found which can be used for identifying five vertical upward gas-liquid two-phase flow patterns. To investigate the dynamic characteristics of gas-liquid two-phase flow, we construct 50 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of gas-liquid two-phase flow. Furthermore, we construct FSCN and demonstrate how network statistic can be used to reveal the fluid structure of gas-liquid two-phase flow. In this paper, from a different perspective, we not only introduce complex network theory to the study of gas-liquid two-phase flow but also indicate that complex network may be a powerful tool for exploring nonlinear time series in practice.

In this chapter, we provide a basic introduction to complex and adaptive dynamical networks. We introduce graph theoretical preliminaries along with different classes of networks studied in the subsequent chapters. We discuss different types of models and coupling schemes. Further, we show how adaptivity is modeled in the context of synaptic plasticity and outline the reduction to phase oscillator models with phase difference-dependent plasticity.

This manuscript explores the analysis of passivity and synchronization criteria for a complex fractional-order dynamical network model with derivative couplings and time-varying delays. The passivity problem of the proposed network model is deduced using various inequality methods and presented as a linear matrix inequality. To ensure complete synchronization for a fractional-order complex dynamical network with derivative couplings (CDNMDC), we derive suitable criteria using an adaptive feedback control method. Additionally, we investigate the synchronization criterion of these complex networks while accounting for parameter uncertainties. Finally, we provide an example to demonstrate the effectiveness of the proposed solutions.

This paper deals with the function projective synchronization of two complex dynamic networks with unknown sector nonlinear input, multiple time-varying delay couplings, model uncertainty, and external interferences. Based on Lyapunov stability theory and inequality transformation method, the robust adaptive synchronization controller is designed, by which the drive and response systems can achieve synchronization according to the function scaling factor. Different from some existing studies on nonlinear system with sector nonlinear input, this paper studies the synchronization of two complex dynamic networks when the boundary of sector nonlinear input is unknown. The controller does not include the boundary value of the sector nonlinear input and the time delay term, so it is more practical and relatively easy to implement. The corresponding simulation examples demonstrate the effectiveness of the proposed scheme.