Development Of Complex Network Research Articles

Exploring the Entropy Complex Networks with Latent Interaction.

In the present work, we study the introduction of a latent interaction index, examining its impact on the formation and development of complex networks. This index takes into account both observed and unobserved heterogeneity per node in order to overcome the limitations of traditional compositional similarity indices, particularly when dealing with large networks comprising numerous nodes. In this way, it effectively captures specific information about participating nodes while mitigating estimation problems based on network structures. Furthermore, we develop a Shannon-type entropy function to characterize the density of networks and establish optimal bounds for this estimation by leveraging the network topology. Additionally, we demonstrate some asymptotic properties of pointwise estimation using this function. Through this approach, we analyze the compositional structural dynamics, providing valuable insights into the complex interactions within the network. Our proposed method offers a promising tool for studying and understanding the intricate relationships within complex networks and their implications under parameter specification. We perform simulations and comparisons with the formation of Erdös-Rényi and Barabási-Alber-type networks and Erdös-Rényi and Shannon-type entropy. Finally, we apply our models to the detection of microbial communities.

Control core of undirected complex networks

With the development of complex networks, many researchers have paid greater attention to studying the control of complex networks over the last decade. Although some theoretical breakthroughs allow us to identify all driver nodes, we still lack an efficient method to identify the driver nodes and understand the roles of individual nodes in contributing to the control of a large complex network. Here, we apply a leaf removal process (LRP) to find a substructure of an undirected network, which is considered as the control core of the original network. Based on a strict mathematical proof, the control core obtained by the LRP has the same controllability as the original network, and it contains at least one set of driver nodes. With this method, we systematically investigate the structural property of the control core with respect to different average degrees of the original networks ($\langle k \rangle$). We denote the node density ($n_\text{core}$) and link density ($l_\text{core}$) to characterize the control core when applying the LRP, and we study the impact of $\langle k \rangle$ on $n_\text{core}$ and $l_\text{core}$ in two artificial networks: undirected Erd\"os-R\'enyi (ER) random networks and undirected scale-free (SF) networks. We find that $n_\text{core}$ and $l_\text{core}$ both change nonmonotonously with increasing $\langle k \rangle$ in the two typical undirected networks. With the aid of core percolation theory, we can offer the theoretical predictions for both $n_\text{core}$ and $l_\text{core}$ as a function of $\langle k \rangle$. Then, we recognize that finding the driver nodes in the control core is much more efficient than in the original network by comparing $n_{\text{D}}$, the controllability of the original network, and $n_{\text{core}}$, regardless of how $\langle k \rangle$ increases.

Multi-factor network modeling and simulation analysis of time-series dynamic and static climate

With the development of complex networks and big data disciplines, complex networks and big data are widely used in various fields to reveal the changing characteristics of things. This paper applies the method of complex network to climatology, explores the complex interaction of various climatic factors in China’s regional climate system with the method of complex network, selects the meteorological big data of 288 meteorological stations in China from 1984 to 2016, defines the meteorological stations and connecting network under various climatic factors as a single factor climate network, and adds the connecting edge between meteorological stations with different climatic factors on this basis, Then the multi-factor climate network is constructed by the network characteristic parameters. The analysis of correlation and stability shows that the correlation of climate network from strong to weak is spring, autumn, winter and summer; In the process of 33 years of time series change, the correlation of climate network shows a weak downward trend, with strong stability and good viability.

Multiservice Reliability Evaluation Algorithm Considering Network Congestion and Regional Failure Based on Petri Net

With the development of complex networks and with increasing service demands, service use is becoming more complex and the composition of services is becoming more complicated. In the XaaS (X as a Service) environment, users only care about the QoE of a service and do not care about the composition process of the service. Therefore, it is important to evaluate the reliability of the entire service. In this article, we use Petri Net as a basis for modeling the composition of services. In addition, we consider the problems of shared resources and common cause faults. Both of these problems can cause network congestion and regional failures. We use distance to assess the effects of regional faults and queuing theory to simulate the network congestion process. Moreover, in the simulation, we verify the impacts of regional failures and network congestion on service reliability. We choose the Tree-Based Search algorithm and the Semi-Markov Model as comparison algorithms. The results of our algorithm are related to service time. Our algorithm can timely reflect the impact of regional failure or network congestion, and it can feedback different evaluation results according to environmental changes. Therefore, our algorithm is more comprehensive and has better performance.

Internet of Things as Complex Networks

In the past few years, the development of complex networks has stimulated great interest in the study of the principles and mechanisms underlying the Internet of things (IoT). Complex networks have led to significant advances in such an active research field and have been proposed to tackle the associated challenges. From a systematic perspective, complex networks pursue new ways to represent, characterize, and analyze the connections and interactions between a huge volume of sensors, actuators, and processors. The theoretical framework and computational tools for complex networks derive new approaches in analyzing IoT architectures, networks, and services. The convergence of empirical and computational advances opens new frontiers of IoT governance and management, covering network dynamics, resource allocation, and task scheduling across heterogeneous networks. We explore the latest technical trends in modelling and analyzing IoT systems with complex networks to provide a better systematic view and more comprehensive understanding of a variety of IoT technologies spanning from sensors and networks to applications and services.

Exploring node importance evolution of weighted complex networks in urban rail transit

With the development of complex networks in urban rail transit (URT), the topological structure changes accordingly and node importance also redistributes dynamically. However, many deficiencies exist in the single measure or unweighted network or static network when ranking node importance. Most importantly, the evolution mechanism of node importance with the network development is seldom studied. In view of this, in this paper, six unweighted and weighted complex networks are firstly modeled in the evolution of URT networks. One of Multiple Attribute Decision Making (MADM) methods is proposed, that is WTOPSIS (The Weighted Technique for Order of Preference by Similarity to Ideal Solution) algorithm combining Coefficient of Variation method and TOPSIS. Then four local and global centralities are aggregated and utilized in WTOPSIS to rank the node importance in those six networks. On the basis, the intersection degrees among the ranking sets are calculated to evaluate the similarities of ranking results. Furthermore, the factors contributing to the evolution of node importance are discussed quantitatively and qualitatively with examples. Finally, the feasibility of the method is verified by the Shenzhen Metro system in 2016. Results show that WTOPSIS algorithm outperforms the single attribute in ranking node importance, which makes up for the shortcomings in existing studies. Besides, for different stations in URT network development, node importance evolution is affected differently by the changes of topological structure and passenger flow. It is necessary to combine with the actual situations for the specific analysis. This study reveals the evolution mechanism of the node importance in the development of URT networks and it also has great theoretical and practical significance.

Analysis of Cascade Fault Optimization Based on Regional Fault and Traffic Reallocation in Complex Networks

With the development of complex networks and demand for information services, more and more businesses are turning to complex networks for their hosting needs. Due to the growing service, attention must be paid to the problem of complex network cascade failures. A cascade failure is a large-scale failure caused by several small-scale failures. This phenomenon initially attracted attention in the power grid field. Currently, complex network cascade failures also deserve attention. In this paper, we focus on mitigating the impact of cascade failures. First, we propose a model to simulate the cascade fault propagation process based on a virus propagation model. We introduce an SDN control plane to the communication network to monitor the overall network. We consider regional failures and optimize traffic redistribution strategies to reduce the impact of cascade failure. Finally, we compare several simulation results based on proposed fault propagation models. The mechanism, we proposed, shows better performance in the remaining available nodes and the remaining network traffic.

Bootstrap percolation on bipartite networks

Bootstrap percolation was first used in statistic physics to study the phenomenon that magnetic-order goes down and disappears because of the disturbance of nonmagnetic impurity. With the development of complex network, the application of bootstrap percolation in network has attracted much attention. In the real world, many systems naturally exhibit the two-branch structure. And bipartite network is one of important networks in complex networks. In this paper, we use the dynamics equation and computational simulation to study the bootstrap percolation in bipartite networks. The parameters we focus on are the node initial active ratios f1 and f2 and active thresholds Ω1, and Ω2. We draw the conclusion that the ratio of active nodes has discontinuous transition, which will gradually disappear with parameters varying. We also prove the consistency between the dynamic equation and simulation results.

Trastornos inespecíficos del nivel de conciencia de seis meses de evolución

With the development of complex networks in urban rail transit (URT), the topological structure changes accordingly and node importance also redistributes dynamically. However, many deficiencies exist in the single measure or unweighted network or static network when ranking node importance. Most importantly, the evolution mechanism of node importance with the network development is seldom studied. In view of this, in this paper, six unweighted and weighted complex networks are firstly modeled in the evolution of URT networks. One of Multiple Attribute Decision Making (MADM) methods is proposed, that is WTOPSIS (The Weighted Technique for Order of Preference by Similarity to Ideal Solution) algorithm combining Coefficient of Variation method and TOPSIS. Then four local and global centralities are aggregated and utilized in WTOPSIS to rank the node importance in those six networks. On the basis, the intersection degrees among the ranking sets are calculated to evaluate the similarities of ranking results. Furthermore, the factors contributing to the evolution of node importance are discussed quantitatively and qualitatively with examples. Finally, the feasibility of the method is verified by the Shenzhen Metro system in 2016. Results show that WTOPSIS algorithm outperforms the single attribute in ranking node importance, which makes up for the shortcomings in existing studies. Besides, for different stations in URT network development, node importance evolution is affected differently by the changes of topological structure and passenger flow. It is necessary to combine with the actual situations for the specific analysis. This study reveals the evolution mechanism of the node importance in the development of URT networks and it also has great theoretical and practical significance.