Typical Scale-free Network Research Articles

Analysis of the gold fixing price fluctuation in different times based on the directed weighted networks

According to the complex network theory, this paper constructs the gold fixing price fluctuation directed weighted network (GFPFDWN) at 10:30 a.m. (A.M.) and 15:00p.m. (P.M.) London Greenwich Mean Time, and studies the law of the gold fixing price fluctuation by analyzing the basic statistical data and characteristics of the GFPFDWN. The results show that the abnormal distribution of the gold fixing price (GFP) at A.M. and P.M. is confirmed by the statistics, the core fluctuation state of the GFPFDWN is reflected in the first 1.8% nodes, and most of the nodes have smaller strength, only a few nodes have larger strength, which has the characteristics of a typical scale-free network. Meanwhile, the nodes with a large strength are closely related among them, which must appear earlier, but the nodes appearing early are not necessarily the nodes with a large strength. The nodes of the GFPFDWN present a short-range correlation in different periods, and the cumulative time of the new nodes shows a high linear growth trend. In addition, the nodes of the GFPFDWN show the characteristics with a small betweenness, clustering coefficient and node strength in different periods, which are different from the characteristics of the random network and chaotic network. When these nodes with small strength appear, which means that this period is in a transitional period, then identifying and analyzing these nodes can effectively predict the fluctuation of the gold fixing price in the next period.

Urban Rail Transit Network Vulnerability Measurement Based on Complex Network Theory: A Case Study of Chongqing Rail Transit

With the rapid growth of social economy, urban rail transit has become a major mode for public transportation, which puts forward higher requirements for the safety of urban rail transit. It is urgent for rail transit operators to find out the key stations in the network and to take corresponding preventive measures in order to avoid heavy losses. Based on the theory of complex network, the study of urban rail transit network vulnerability was carried out through computer simulation at different attack strategies to find out the influence of failing key stations to entire network. Then, a case study of Chongqing urban rail transit network was carried out. Both random attack strategy and selected attack strategy were applied to the network. By comparing the relative efficiency declines of two attack strategies, it is concluded that Chongqing rail transit network is a typical scale-free network, and is robust under random attacks, and is vulnerable under selected attacks. Furthermore, the results show that most of the important stations are stations with large degree.

Academic Community Network Analysis of Artificial Neural Network

Artificial neural network is one of the hottest research areas in recent years. Inspired by the system of human brains, Artificial neural network is widely used in expert systems, machine learning and other fields. In recent years, the artificial neural networks related literature shows explosive growth. Through social network research on the artificial neural networks can help us to understand and predict trends and changes in the field of neural network. In this paper, a social network of co-writing is built based on for last 15 years SCI articles, which themed “artificial neural network”. The information from the full record of these papers is abstracted and the discussion on it is shown in two aspects: time period and impact factor. By the analysis and comparison on the nature of the complex network, it can be found that artificial neural networks draw more and more attention, research cooperation in this field is more common, and in the group of authoritative experts this feature is particularly evident. Meanwhile, it is obvious that social network of artificial neural network is a typical scale-free network.

Hybrid modeling and empirical analysis of automobile supply chain network

Based on the connection mechanism of nodes which automatically select upstream and downstream agents, a simulation model for dynamic evolutionary process of consumer-driven automobile supply chain is established by integrating ABM and discrete modeling in the GIS-based map. Firstly, the rationality is proved by analyzing the consistency of sales and changes in various agent parameters between the simulation model and a real automobile supply chain. Second, through complex network theory, hierarchical structures of the model and relationships of networks at different levels are analyzed to calculate various characteristic parameters such as mean distance, mean clustering coefficients, and degree distributions. By doing so, it verifies that the model is a typical scale-free network and small-world network. Finally, the motion law of this model is analyzed from the perspective of complex self-adaptive systems. The chaotic state of the simulation system is verified, which suggests that this system has typical nonlinear characteristics. This model not only macroscopically illustrates the dynamic evolution of complex networks of automobile supply chain but also microcosmically reflects the business process of each agent. Moreover, the model construction and simulation of the system by means of combining CAS theory and complex networks supplies a novel method for supply chain analysis, as well as theory bases and experience for supply chain analysis of auto companies.

Hypergraph partitioning for social networks based on information entropy modularity

A social network is a typical scale-free network with power-law degree distribution characteristics. It demonstrates several natural imbalanced clusters when it is abstracted as a graph, and expands quickly under its generative mechanism. Hypergraph is superior for modeling multi-user operations in social networks, and partitioning the hypergraph modeled social networks could ease the scaling problems. However, today's popular hypergraph partitioning tools are not sufficiently scalable; thus, unable to achieve high partitioning quality for naturally imbalanced datasets. Recently proposed hypergraph partitioner, hyperpart, replaces the balance constraint with an entropy constraint to achieve high-fidelity partitioning solutions, but it is not tailored for scale-free networks, like social networks. In order to achieve scalable and high quality partitioning results for hypergraph modeled social networks, we propose a partitioning method, EQHyperpart, which utilizes information-Entropy-based modularity Q value (EQ) to direct the hypergraph partitioning process. This EQ considers power-law degree distribution while describing the “natural” structure of scale-free networks. We then apply simulated annealing and introduce a new definition of hyperedge cut, micro cut, to avoid the local minima in convergence of partitioning, developing EQHyperpart into two specific partitioners, namely: EQHyperpart-SA and EQHyperpart-MC. Finally, we evaluate the utility of our proposed method using classical social network datasets, including Facebook dataset. Findings show that EQHyperpart partitioners are more scalable than competing approaches, achieving a tradeoff between modularity retaining and cut size minimizing under balance constraints, and an auto-tradeoff without balance constraints for hypergraph modeled social networks.

Evolving Scale-Free Networks by Poisson Process: Modeling and Degree Distribution.

Since the great mathematician Leonhard Euler initiated the study of graph theory, the network has been one of the most significant research subject in multidisciplinary. In recent years, the proposition of the small-world and scale-free properties of complex networks in statistical physics made the network science intriguing again for many researchers. One of the challenges of the network science is to propose rational models for complex networks. In this paper, in order to reveal the influence of the vertex generating mechanism of complex networks, we propose three novel models based on the homogeneous Poisson, nonhomogeneous Poisson and birth death process, respectively, which can be regarded as typical scale-free networks and utilized to simulate practical networks. The degree distribution and exponent are analyzed and explained in mathematics by different approaches. In the simulation, we display the modeling process, the degree distribution of empirical data by statistical methods, and reliability of proposed networks, results show our models follow the features of typical complex networks. Finally, some future challenges for complex systems are discussed.

Maximum entropy networks are more controllable than preferential attachment networks

A maximum entropy (ME) method to generate typical scale-free networks has been recently introduced. We investigate the controllability of ME networks and Barabási–Albert preferential attachment networks. Our experimental results show that ME networks are significantly more easily controlled than BA networks of the same size and the same degree distribution. Moreover, the control profiles are used to provide insight into control properties of both classes of network. We identify and classify the driver nodes and analyze the connectivity of their neighbors. We find that driver nodes in ME networks have fewer mutual neighbors and that their neighbors have lower average degree. We conclude that the properties of the neighbors of driver node sensitively affect the network controllability. Hence, subtle and important structural differences exist between BA networks and typical scale-free networks of the same degree distribution.

Pinning Control of Enterprise Marketing Network Based on Complex Network Theory

Complex enterprise marketing network is typical scale-free network. Based on the non-uniformity of enterprise marketing network topology, we select pinning control strategy. There are network members for selection a certain percentage to exercise control, to use of virtual control action associated with the coupling between network members, making large-scale enterprise marketing network reaches a steady state. Paper showed that pinning control is effectiveness to control of enterprise marketing for network.

Research of Enterprise Project Chain Risk Element Transmission Based on Complex Network Model

In order to research of enterprise project chain risk element transmission, constructed the enterprise project chain network model based on a complex network and analyzed the complex network characteristics. Take the mean-field theory to analyze the project chain risk element transmission and get the critical value of project chain transmission. The simulation results show that the project chain has the typical characteristics of a complex network. The distribution of it obeys the power law with lower average path length and higher clustering coefficient. It is a typical scale-free network. Based on randomly selecting risk nodes and the key risk nodes, simulate the relationship between transfer rate and the density of project chain risk nodes. It can provide reference for enterprise to make decision of project chain risk prevention. Finally, improve the whole project chain risk management efficiency and valuably.

Tuning degree distributions: Departing from scale-free networks

Scale-free networks are characterized by a degree distribution with power-law behavior. Although scale-free networks have been shown to arise in many areas, ranging from the World Wide Web to transportation or social networks, degree distributions of other observed networks often differ from the power-law type. Data based investigations require modifications of the typical scale-free network. We present an algorithm that generates networks in which the shape of the degree distribution is tunable by modifying the preferential attachment step of the Barabási-Albert construction algorithm. The shape of the distribution is represented by dispersion measures such as the variance and the skewness, both of which are highly correlated with the maximal degree of the network and, therefore, adequately represents the influence of superspreaders or hubs. By combining our algorithm with work of Holme and Kim, we show how to generate networks with a variety of degree distributions and clustering coefficients.

Immunization for scale-free networks by random walker

Based on the random walk and the intentional random walk, we propose two types of immunization strategies which require only local connectivity information. On several typical scale-free networks, we demonstrate that these strategies can lead to the eradication of the epidemic by immunizing a small fraction of the nodes in the networks. Particularly, the immunization strategy based on the intentional random walk is extremely efficient for the assortatively mixed networks.