Explore the vast range of titles available.

An urban public traffic network is a typical high-order complex network. There are multiple types of transportation in an urban public traffic network, and each type has different impacts on urban transportation. Robustness analyses of urban public traffic networks contribute to the safe maintenance and operation of urban traffic systems. In this paper, a new cascading failure model for urban public traffic networks is constructed based on a multi-subnet composite complex network model. In order to better simulate the actual traffic flow in the composite network, the concept of traffic function is proposed in the model. Considering the different effects of various relationships on nodes in the composite network, the traditional cascading failure model has been improved and a deliberate attack strategy and a random attack strategy have been adopted to study the robustness of the composite network. In the experiment, the urban bus–subway composite network in Qingdao, China, was used as an example for simulation. The experimental results showed that under two attack strategies, the network robustness did not increase with the increase in capacity, and the proportion of multiple relationships had a significant impact on the network robustness.

Scientists strive to understand how functionalities, such as conservation laws, emerge in complex systems. Living complex systems in particular create high-ordered functionalities by pairing up low-ordered complementary processes, e.g., one process to build and the other to correct. We propose a network mechanism that demonstrates how collective statistical laws can emerge at a macro (i.e., whole-network) level even when they do not exist at a unit (i.e., network-node) level. Drawing inspiration from neuroscience, we model a highly stylized dynamical neuronal network in which neurons fire either randomly or in response to the firing of neighboring neurons. A synapse connecting two neighboring neurons strengthens when both of these neurons are excited and weakens otherwise. We demonstrate that during this interplay between the synaptic and neuronal dynamics, when the network is near a critical point, both recurrent spontaneous and stimulated phase transitions enable the phase-dependent processes to replace each other and spontaneously generate a statistical conservation law—the conservation of synaptic strength. This conservation law is an emerging functionality selected by evolution and is thus a form of biological self-organized criticality in which the key dynamical modes are collective.

Social network analysis provides innovative techniques to analyze interactions among entities by emphasizing social relationships. Diffusion in the social network can be referred to spread of information among interconnected nodes or entities in a network. The rate and intensity of diffusion depend upon network topology and initialization of network parameters. Individual nodes act as source of motivation for others in the diffusion process. The epidemic model is one of the basic diffusion models that helps in analyzing the transmission of infectious disease from one person to another through social connections. This can be further generalized for other socially connected platforms involving information exchange. In our research, we have proposed a diffusion methodology for tracking the rate with which information spread over underlying social interaction structure, with variation in time and other social parameters. In addition to forward state transitions, recoverable transition is also proposed, which allows a node currently under influence of incoming information, to revert back to previous state of perception. The proposed model also assists in predicting the fraction of population getting diffused over real and large-scale complex network for specific temporal domain.

Determining the core structure of complex network systems allows us to simplify them. Using h- bridge and h -strength measurements in a weighted network, we extract the h- backbone core structure. We find that focusing on the h -backbone in a network allows greater simplification because it has fewer edges and thus fewer adjacent nodes. We examine three practical applications: the co-citation network in an information system, the open flight network in a social system, and coauthorship in network science publications.

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.

This article proposes a systematic review of papers dealing with maritime traffic whose methodology is based on a Complex Network Analysis (CNA) approach. The papers selected have been categorised, reviewed and analysed, extracting the most important characteristics of each, namely sources of information, geographical scope and network modelling characteristics, and then grouped according to the specific topics covered. The literature is classified according to two main streams: Papers dealing with a topological description of maritime networks, and papers applying a CNA approach to specific topics. In each case, an analysis is carried out, highlighting the most interesting concepts and methodologies of the selected papers, summarising the most important findings and proposing further topics for investigation. An additional analysis has been carried out with the keywords of the selected papers, creating a network and identifying seven communities representing and grouping the main research interest topics in the selected literature.

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.

Many networks generated by nature have two generic properties: they are formed in the process of preferential attachment and they are scale-free. Considering these features, by interfering with mechanism of the preferential attachment, we propose a generalisation of the Barabási–Albert model—the ’Fractional Preferential Attachment’ (FPA) scale-free network model—that generates networks with time-independent degree distributions p ( k ) ∼ k − γ with degree exponent 2 < γ ≤ 3 (where γ = 3 corresponds to the typical value of the BA model). In the FPA model, the element controlling the network properties is the f parameter, where f ∈ ( 0 , 1 ⟩ . Depending on the different values of f parameter, we study the statistical properties of the numerically generated networks. We investigate the topological properties of FPA networks such as degree distribution, degree correlation (network assortativity), clustering coefficient, average node degree, network diameter, average shortest path length and features of fractality. We compare the obtained values with the results for various synthetic and real-world networks. It is found that, depending on f, the FPA model generates networks with parameters similar to the real-world networks. Furthermore, it is shown that f parameter has a significant impact on, among others, degree distribution and degree correlation of generated networks. Therefore, the FPA scale-free network model can be an interesting alternative to existing network models. In addition, it turns out that, regardless of the value of f, FPA networks are not fractal.

Based on a method of network split according to the different nature of time-delay, the model of the united directed complex networks with multi-links are established. The exponential synchronisation of this model via adaptive periodically intermittent control is further investigated in this study. Via the Lyapunov stability theory combined with the method of the adaptive control, pinning control and periodically intermittent control, some novel and simple criteria are derived for the global exponential synchronisation of the complex dynamical networks with multi-links, and the derived results are less conservative. Several corresponding adaptive feedback synchronisation controllers, pinning controllers and impulsive controllers are designed, respectively. The restriction on the control width and the time delay are removed. This leads to a larger application scope for this method. Using chaotic system as the nodes of the networks, some numerical examples of the united directed complex networks with multi-links and delayed complex networks are given to demonstrate the effectiveness of the control strategies, respectively.

Network science has made tremendous advances, allowing the modeling of complex real-world systems. Although networks include sophisticated community structures by definition, discovering and understanding these communities remains a challenging endeavor, compounded by the need to navigate a multidisciplinary terrain. This comprehensive survey serves as a contemporary guide and systematically presents an up-to-date survey of community detection methods, meticulously categorizing them for a comprehensive understanding. Through critical analysis, we assess the strengths, weaknesses, and performance metrics of various algorithms, ranging from classical techniques to cutting-edge methods designed to address the complexities of overlapping community detection. Additionally, we explore emerging trends in dynamic community detection, including techniques like Temporal Motif Analysis and Continuous-Time Models. We additionally present an in-depth evaluation of these methods, examining their performance on both artificial benchmarks and real-world networks. This review also sheds light on the diverse applications of community detection across domains such as sociology, biology, education, and technology. By presenting a holistic view of community detection, we aim to facilitate researchers’ and practitioners’ access to this crucial field, addressing the theoretical and practical application gaps, and contributing to the continued evolution of network science.