Degree Distribution Characteristics Research Articles

Analysis on the diffusion of RFID technological innovation from the perspective of disruptive innovation

ABSTRACT Disruptive technology has profoundly affected the market environment and business patterns. Traditional and emerging industries are willing to adopt it to nurture their innovation activities and achieve competitive advantage. However, traditional technological innovation theory cannot give accurate guidance because of the peculiarities of disruptive technology. Thus, in this study, we explore the diffusion mechanism of radio-frequency identification (RFID) technological innovation from the perspective of disruptive innovation. We implement maximum likelihood fitting methods and the goodness-of-fit test to analyse the network’s degree distribution characteristics through evolution. Results illustrate that the connection mechanism changes through the evolution process. In particular periods, preferential attachment does not work. Dominant technologies will lose their position with the emergence of disruptive technologies, hence replaced by them. The findings will provide practical suggestions to firms in facilitating their strategic decision-making when faced with disruptive technologies.

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.

A Core Model for Parts Suppliers Selecting Method in Manufacturing Supply Chain

Service-oriented manufacturing is the new development of manufacturing systems, and manufacturing supply chain service is also an important part of the service-oriented manufacturing systems; hence, the optimal selection of parts suppliers becomes one of key problems in the supply chain system. Complex network theories made a rapid progress in recent years, but the classical models such as BA model and WS model can not resolve the widespread problems of manufacturing supply chain, such as the repeated attachment of edge and fixed number of vertices, but edges increased with preferential connectivity, and flexible edges’ probability. A core model is proposed to resolve the problem in the paper: it maps the parts supply relationship as a repeatable core; a vertex’s probability distribution function integrating the edge’s rate and vertex’s degree is put forward; some simulations, such as the growth of core, the degree distribution characteristics, and the impacting of parameter, are carried out in our experiments, and the case study is set also. The paper proposed a novel model to analyze the manufacturing supply chain system from the insights of complex network.

A Fault Tolerance Optimization Model of the China Railway Geographic Network Topological Structure

With degree distribution characteristics of the China Railway geographic network, an optimization model from macro to micro is established on the relative entropy. Firstly, Poisson distribution of the China Railway geographic network is verified and fitted. Secondly, the “Twelfth Five-Year Plan” of the railway geographic network is chosen as an example on which a macro model is built. Finally, the “Twelfth Five-Year Plan” of Guangzhou Railway Group’s geographic network is chosen as the other example on which a micro model is built and our optimization scheme is proposed. Results reveal that, for improving the railway network fault tolerance, from the macroscopic aspect the “Twelfth Five-Year Plan” railway network should strengthen the railway agglomeration degree; then the microscopic optimization model is able to improve the fault tolerance effectively.

Does AS size determine degree in as topology?

In a recent and much celebrated paper, Faloutsos <i>et al.</i> [6] found that the inter Autonomous System (AS) topology exhibits a power-law degree distribution. This result was quite unexpected in the networking community, and stirred significant interest in exploring the possible causes of this phenomenon. The work of Barabasi <i>et al.</i> [2], and its application to network topology generation in the work of Medina <i>et al.</i> [9], have explored a promising class of models that yield strict power-law degree distributions. These models, which we will refer to collectively as the <i>B-A model</i>, describe the detailed dynamics of the network growth process, modeling the way in which connections are made between ASs. There are two simple connectivity rules that define the evolution of AS connectivity over time: <i>incremental growth</i> where a new AS connects to existing ASs, and <i>preferential connectivity</i> where the likelihood of connecting to an AS is proportional to the vertex outdegree of the target AS. These simple rules, which are similar to the classical "rich get richer" model originally proposed by Simon [12], lead to power-law degree distributions. While the B-A model provably yields power-law vertex degree distributions, recent empirical evidence indicates that the model may not be consistent with the dynamics underlying the evolution of the actual AS topology. First, there is strong evidence [3, 4] that the degree distribution of the actual AS topology does not conform to a strict power law. However, the distribution is certainly <i>heavy-tailed</i> or <i>highly-variable</i> in the sense that the observed vertex degrees typically range over three or four orders of magnitude; in some cases, the <i>tail</i> of the degree distribution may fit a power law. These observations were gleaned from more complete pictures of AS - level connectivity (obtained by augmenting BGP route tables with peering relationships from other sources) than those used by earlier work [2, 6, 9]. Second, the B-A model's AS connectivity evolution rules can be shown to be inconsistent with empirical AS growth measurements [16]. As such, while the B-A model appears to produce topologies whose degree distribution characteristics exhibit power-law behavior, it cannot be a valid <i>explanation</i> for the connectivity evolution in the AS topology. Clearly, some of these empirical observations don't corroborate the claim that the B-A model explains the phenomenon of highly variable vertex degrees in the Internet's AS topology [2]. However, the B-A model was originally proposed as a simple illustration of how some elementary mechanisms or rules can give rise to power law vertex degree distributions. As such, it is likely that the B-A model can be modified to accommodate these more recent findings [1], but we will neither discuss here such modifications nor comment on their possibility for success. Instead, we merely note that any such resulting model would seek, as does the original B-A model, to explain the highly variable degree distribution of the AS topology through the detailed dynamics of how connections between ASs are established. The purpose of this note is to raise the question --- motivated by the B-A approach---of whether the underlying cause of the high variability phenomenon of the vertex degree distribution lies in the detailed dynamics of network growth, or if there are alternative explanations. To that end, we briefly outline an alternative explanation for the AS topology degree distribution. We do not claim to have proven that this explanation holds; our purpose here is merely to expand the dialog to a larger class of explanations for the variability of the AS topology degree distribution.