Ising model on evolution networks and its application on opinion formation

Many phenomena show that in a favorable circumstance an agent still has an updating possibility, and in an unfavor- able circumstance an agent also has a possibility of holding its own state and reselecting its neighbors. To describe this kind of phenomena an Ising model on evolution networks was presented and used for consensus formation and separation of opinion groups in human population. In this model the state-holding probability p and selection-rewiring probability q were introduced. The influence of this mixed dynamics of spin flips and network rewiring on the ordering behavior of the model was investigated, p hinders ordering of opinion networks and q accelerates the dynamical process of networks. Influence of q on the ordering and separating stems from its effect on average path length of networks.

Structural Characteristics and Evolution of a Weighted Sino-US Container Shipping Network

This study selected the Sino-US route data from the top 30 global container liner companies between December 1,2019,and December 29,2019,as the data source utilizing the complex network research methodology.It constructs a Sino-US container shipping network through voyage weighting and analyzes the essential structural characteristics to explore the network’s complex structural fea-tures.The network’s evolution is examined from three perspectives,namely,time,space,and event influence,aiming to comprehens-ively explore the network’s evolution mechanism.The results revealed that:1)the weighted Sino-US container shipping network exhib-its small-world and scale-free properties.Key hub ports in the United States include NEW YORK NY,SAVANNAH GA,LOS ANGELES CA,and OAKLAND CA,whereas SHANGHAI serving as the hub port in China.The geographical distribution of these hub ports is uneven.2)Concerning the evolution of the weighted Sino-US container shipping network,from a temporal perspective,the evolution of the regional structure of the entire Sino-US region and the Inland United States is in a stage of radiative expansion and de-velopment,with a need for further enhancement in competitiveness and development speed.The evolution of the regional structure of southern China and Europe is transitioning from the stage of radiative expansion and development to an advanced equilibrium stage.The shipping development in Northern China,the Western and Eastern United States,and Asia is undergoing significant changes but faces challenges of fierce competition and imbalances.From a spatial perspective,the rationality and effectiveness of the improved weighted Barrat-Barthelemy-Vespignani(BBV)model are confirmed through theoretical derivation.The applicability of the improved evolution model is verified by simulating the evolution of the weighted Sino-US container shipping network.From an event impact per-spective,the Corona Virus Disease 2019(COVID-19)pandemic has not fundamentally affected the spatial pattern of the weighted Sino-US container shipping network but has significantly impacted the network’s connectivity.The network lacks sufficient resilience and stability in emergency situations.3)Based on the analysis of the structural characteristics and evolution of the weighted Sino-US con-tainer shipping network,recommendations for network development are proposed from three aspects:emphasizing the development of hub ports,focusing on the balanced development of the network,and optimizing the layout of Chinese ports.

Exploring link generation in a temporal evolution network for human mobility

The prevalence of mobile devices has spurred human mobility to be applied in mobile networking and communications by using network science, in which the temporal evolution of a network topology is of great importance for protocol design and performance analysis. This paper focuses on link generation in a temporal evolution network. Based on observations revealing the strong correlation between the connection patterns of different time periods, a link generation potential based on historical connections is proposed in this paper, aiming to provide a method for making topological predictions with less randomness. Using MIT Reality dataset, an evaluation of the accuracy of the proposed method was conducted. The experimental results demonstrate the proposal's adequacy in terms of its accuracy.

Research on E-mail Communication Network Evolution Model Based on User Information Propagation

E-mail communication network evolution model based on user information propagation is studied. First, mathematical representation of weighted e-mail communication network is proposed, and network center parameters of Enron dataset and the distribution of node degree and strength are analyzed. Then, some rules of e-mail communication network evolution are found. Second, the model of e-mail information propagation is described, and e-mail communication network evolution model based on user information propagation is proposed. Lastly, the simulation proves the correctness of the distribution characteristic of degree and strength of the model proposed and then verifies that the model proposed is closer to the real situation of e-mail communication network through parameter comparison. This research provides the basis for other researches on social network evolution and data communication.

The Study on the Evolutional Mechanics of Technological Innovation Network

Based on the statistical mechanics and technology innovation network, this paper analyzed network evolutional mechanics, and constituted its evolutional model.

Network evolution driven by dynamics applied to graph coloring

An evolutionary network driven by dynamics is studied and applied to the graph coloring problem. From an initial structure, both the topology and the coupling weights evolve according to the dynamics. On the other hand, the dynamics of the network are determined by the topology and the coupling weights, so an interesting structure-dynamics co-evolutionary scheme appears. By providing two evolutionary strategies, a network described by the complement of a graph will evolve into several clusters of nodes according to their dynamics. The nodes in each cluster can be assigned the same color and nodes in different clusters assigned different colors. In this way, a co-evolution phenomenon is applied to the graph coloring problem. The proposed scheme is tested on several benchmark graphs for graph coloring.

Evolution of a protein domain interaction network

In this paper, we attempt to understand complex network evolution from the underlying evolutionary relationship between biological organisms. Firstly, we construct a Pfam domain interaction network for each of the 470 completely sequenced organisms, and therefore each organism is correlated with a specific Pfam domain interaction network; secondly, we infer the evolutionary relationship of these organisms with the nearest neighbour joining method; thirdly, we use the evolutionary relationship between organisms constructed in the second step as the evolutionary course of the Pfam domain interaction network constructed in the first step. This analysis of the evolutionary course shows： （i） there is a conserved sub-network structure in network evolution; in this sub-network, nodes with lower degree prefer to maintain their connectivity invariant, and hubs tend to maintain their role as a hub is attached preferentially to new added nodes; （ii） few nodes are conserved as hubs; most of the other nodes are conserved as one with very low degree; （iii） in the course of network evolution, new nodes are added to the network either individually in most cases or as clusters with relative high clustering coefficients in a very few cases.

A Dynamic Evolution Model of Airline Networks

Empirical data show that most of the degree distribution of airline networks assume a double power law. In this work, firstly, we assume cities as sites, flight between two cities as an edge between two sites, and build a dynamic evolution model for airline networks by improving the BA model, in which the conception of attractiveness plays a decisive role in the course of evolution of the networks. To this end, we discuss whether the attractiveness depends on the site label s or not separately, finally we obtain analytic degree distribution. As a result, if the attractiveness of a site is independent of the degree distribution of sites, which will follow the double power law, otherwise, it will be scale-free. Moreover, degree distribution depends on the parameters of the models, and some parameters aye more sensitive than others.

China Unicom's Experience in Network Evolution towards NGN

The article introduces China Unicom's valuable experience obtained in the construction and operation of its IP-based data and fixed services network. In the construction of the network,China Unicom aimed at the quick deployment of telecom-level integrated services by building up multi-level metropolitan area networks and access networks;in the operation of the network,China Unicorn made great efforts with equipment manufactures, software developers and system integration providers and achieved stable and improved network performance.

Evolution of Optical Transport Networks and Their Solution in 3G Networks

As new generation mobile networks, 3G networks focus on data services and integrate voice, data and multimedia services. However, traditional Optical Transport Networks (OTNs) cannot meet the requirements of 3G networks anymore, because of their complicated configuration, low bandwidth efficiency, high cost, and bad network and service scalability. The emerging of Multi-Service Transport Platform (MSTP), Wavelength Division Multiplexing (WDM), and Automatically Switched Optical Network (ASON) technologies for optical fiber communications makes up for these weaknesses. The leading solution to 3G access transport networks is the MSTP technology based on Synchronous Digital Hierarchy (SDH), while that to 3G core transport network is ASON+WDM.

Technological Innovation Collaboration Network Evolution Simulation and Empirical Research

The technology innovation cooperation relationship of enterprises has evolved into the representation of complicated network. MATLAB software is used for computer simulation on network evolution process of technology innovation cooperation, and Ucinet characterization analysis software is used to compute characteristic values of simulation network such as power law index of network, average route length and accumulation coefficients. It is compared with the actual network, and the simulation results are analyzed to verify the rationality of the constructed evolution model.

UEs Power Reduction Evolution with Adaptive Mechanism over LTE Wireless Networks

At present, the major drawback for mobile phones is the issue of power consumption. As one of the alternatives to decrease the power consumption of standard, power-hungry location-based services usually require the knowledge of how individual phone features consume power. A typical phone feature is that the applications related to multimedia streaming utilize more power while receiving, processing, and displaying the multimedia contents, thus contributing to the increased power consumption. There is a growing concern that current battery modules have limited capability in fulfilling the long-term energy need for the progress on the mobile phone because of increasing power consumption during multimedia streaming processes. Considering this, in this paper, we provide an offline meaning sleep-mode method to compute the minimum power consumption comparing with the power-on solution to save power by implementing energy rate adaptation(RA) mechanism based on mobile excess energy level purpose to save battery power use. Our simulation results show that our RA method preserves efficient power while achieving better throughput compared with the mechanism without rate adaptation(WRA).

Study on the Impact of Massive Refracturing on the Fracture Network in Tight Oil Reservoir Horizontal Wells

Class III tight oil reservoirs have low porosity and permeability,which are often responsible for low production rates and limited recovery.Extensive repeated fracturing is a well-known technique to fix some of these issues.With such methods,existing fractures are refractured,and/or new fractures are created to facilitate communication with natural fractures.This study explored how different refracturing methods affect horizontal well fracture networks,with a special focus on morphology and related fluid flow changes.In particular,the study relied on the unconventional fracture model(UFM).The evolution of fracture morphology and flow field after the initial fracturing were analyzed accordingly.The simulation results indicated that increased formation energy and reduced reservoir stress differences can promote fracture expansion.It was shown that the length of the fracture network,the width of the fracture network,and the complexity of the fracture can be improved,the oil drainage area can be increased,the distance of oil and gas seepage can be reduced,and the production of a single well can be significantly increased.

Information-Defined Networks:A Communication Network Approach for Network Studies

The research of complex networks facilitates the progress of various disciplines,including biology,chemistry,social science,computer,and communication engineering.Recently,it is popular to utilize complex networks to study the communication networks,such as designing efficient routing strategies and robust communication networks.However,exploiting the advantages of communication networks to investigate networks in various disciplines beyond telecommunications is still in infancy.Because of this situation,this paper proposes an information-defined network(IDN)framework by which a complex network can be abstracted as a communication network associated with multiple intelligent agents.Specifically,each component and dynamic process in this framework can be defined by information.We show that the IDN framework promotes the research of unsolved problems in the current complex network field,especially for detecting new interaction types in realworld networks.

Mean-field Theory for Some Bus Transport Networks with Random Overlapping Clique Structure

Transport networks, such as railway networks and airport networks, are a kind of random network with complex topology. Recently, more and more scholars paid attention to various kinds of transport networks and try to explore their inherent characteristics. Here we study the exponential properties of a recently introduced Bus Transport Networks （BTNs） evolution model with random overlapping clique structure, which gives a possible explanation for the observed exponential distribution of the connectivities of some BTNs of three major cities in China. Applying mean-field theory, we analyze the BTNs model and prove that this model has the character of exponential distribution of the connectivities, and develop a method to predict the growth dynamics of the individual vertices, and use this to calculate analytically the connectivity distribution and the exponents. By comparing mean-field based theoretic results with the statistical data of real BTNs, we observe that, as a whole, both of their data show similar character of exponential distribution of the connectivities, and their exponents have same order of magnitude, which show the availability of the analytical result of this paper.

Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases

Purpose： This study aims to answer the question to what extent different types of networks can be used to predict future co-authorship among authors.Design/methodology/approach： We compare three types ot networks： unwelgntea networks, in which a link represents a past collaboration; weighted networks, in which links are weighted by the number of joint publications; and bipartite author-publication networks. The analysis investigates their relation to positive stability, as well as their potential in predicting links in future versions of the co-authorship network. Several hypotheses are tested.Findings： Among other results, we find that weighted networks do not automatically lead to better predictions. Bipartite networks, however, outperform unweighted networks in almost all cases. Research limitations： Only two relatively small case studies are considered Practical implications： The study suggests that future link prediction studies on networks should consider using the bipartite network as a training network. Originality/value： This is the first systematic comparison of unweighted, weighted, and bipartite training networks in link prediction.

A Weibo local network growth model constructed from the perspective of following-followed

In order to explore the evolution process of the Weibo local network,this study first defines four factors influencing the evolution of the Weibo network.On this basis,the BA scale-free network model was enhanced by incorporating these four factors and accounting for directionality,resulting in a Weibo local network evolution model based on user attributes and behavioral similarity.The model's validity was validated by comparing simulation results with real data.The findings indicate that the Weibo local network exhibits both small-world characteristics and distinctive features.The results show that the Weibo local network exhibits both small-world characteristics and distinctive properties.The in-degree distribution follows a mixed pattern of exponential and power-law distributions,the degree-degree shows isomatching,and both the in-degree centrality and eigenvector centrality values are relatively low.This research contributes to our understanding of user behaviour in the Weibo network,and provides a structural basis for exploring the impact of Weibo network structure on information dissemination.

Scale-Free Properties in Traffic System

In this work, we propose a new model of evolution networks, which is based on the evolution of the traffic flow. In our method, the network growth does not take into account preferential attachment, and the attachment of new node is independent of the degree of nodes. Our aim is that employing the theory of evolution network, we give a further understanding about the dynamical evolution of the traffic flow. We investigate the probability distributions and scaling properties of the proposed model The simulation results indicate that in the proposed model, the distribution of the output connections can be well described by scale-free distribution. Moreover, the distribution of the connections is largely related to the traffic flow states, such as the exponential distribution （i.e., the scale-free distribution） and random distribution etc.

多传感器网络中目标定位的改进算法

为提高多传感器网络中的目标定位精度,基于优胜劣汰的进化思想提出一种改进算法.该算法通过循环运算,剔除明显异常的测距数据后,再对目标点进行定位.首先利用最小二乘法,拟合出目标点的初始概位,然后计算各节点到初始概位的距离与测量数据的相对误差的绝对值及相对误差的平均值,判断该平均值是否达到某一经验值,若未达到经验值,则剔除相对误差最大的节点数据,再进行下一轮运算,直到平均值达到经验值,且确保节点数多于3时,此轮次中估算的概位坐标即为目标点坐标.Matlab仿真实验表明,本算法可有效地提高定位精度.