Abstract: Energy has a prominent role for development of
nations. Countries which have energy resources also have strategic
power in the international trade of energy since it is essential for all
stages of production in the economy. Thus, it is important for
countries to analyze the weaknesses and strength of the system. On
the other side, international trade is one of the fields that are analyzed
as a complex network via network analysis. Complex network is one
of the tools to analyze complex systems with heterogeneous agents
and interaction between them. A complex network consists of nodes
and the interactions between these nodes. Total properties which
emerge as a result of these interactions are distinct from the sum of
small parts (more or less) in complex systems. Thus, standard
approaches to international trade are superficial to analyze these
systems. Network analysis provides a new approach to analyze
international trade as a network. In this network, countries constitute
nodes and trade relations (export or import) constitute edges. It
becomes possible to analyze international trade network in terms of
high degree indicators which are specific to complex networks such
as connectivity, clustering, assortativity/disassortativity, centrality,
etc. In this analysis, international trade of crude oil and coal which
are types of fossil fuel has been analyzed from 2005 to 2014 via
network analysis. First, it has been analyzed in terms of some
topological parameters such as density, transitivity, clustering etc.
Afterwards, fitness to Pareto distribution has been analyzed via
Kolmogorov-Smirnov test. Finally, weighted HITS algorithm has
been applied to the data as a centrality measure to determine the real
prominence of countries in these trade networks. Weighted HITS
algorithm is a strong tool to analyze the network by ranking countries
with regards to prominence of their trade partners. We have
calculated both an export centrality and an import centrality by
applying w-HITS algorithm to the data. As a result, impacts of the
trading countries have been presented in terms of high-degree
indicators.
Abstract: A Vehicular Ad-Hoc Network (VANET) is a mobile Ad-Hoc Network that provides connectivity moving device to fixed equipments. Such type of device is equipped with vehicle provides safety for the passengers. In the recent research areas of traffic management there observed the wide scope of design of new methodology of extension of wireless sensor networks and ad-hoc network principal for development of VANET technology. This paper provides the wide research view of the VANET and MANET concept for the researchers to contribute the better optimization technique for the development of effective and fast atomization technique for the large size of data exchange in this complex networks.
Abstract: Community structures widely exist in almost all real-life networks. Extensive researches have been carried out on detecting community structures in complex networks. However, many aspects of how community structures may affect the dynamics and properties of complex networks still remain unclear. In this work, we examine the impacts of community structures on the epidemic spreading and detection in complex networks. Extensive simulation results show that community structures may not help decrease the infection size at steady state, yet they could indeed help slow down the infection spreading. Also, networks with strong community structures may expect to have a smaller average infection size when equipped with a number of sparsely deployed monitors.
Abstract: This interdisciplinary research aims to distinguish universal scale-free and field-like fundamental principles of selforganization observable across many disciplines like computer science, neuroscience, microbiology, social science, etc. Based on these universal principles we provide basic premises and postulates for designing holistic social simulation models. We also introduce pervasive information field (PIF) concept, which serves as a simulation media for contextual information storage, dynamic distribution and organization in social complex networks. PIF concept specifically is targeted for field-like uncoupled and indirect interactions among social agents capable of affecting and perceiving broadcasted contextual information. Proposed approach is expressive enough to represent contextual broadcasted information in a form locally accessible and immediately usable by network agents. This paper gives some prospective vision how system-s resources (tangible and intangible) could be simulated as oscillating processes immersed in the all pervasive information field.
Abstract: The objective of this work is to explicit knowledge on the interactions between the chlorophyll-a and nine meroplankton larvae of epibenthonic fauna. The studied case is the Arraial do Cabo upwelling system, Southeastern of Brazil, which provides different environmental conditions. To assess this information a network approach based in probability estimative was used. Comparisons among the generated graphs are made in the light of different water masses, application of Shannon biodiversity index, and the closeness and betweenness centralities measurements. Our results show the main pattern among different water masses and how the core organisms belonging to the network skeleton are correlated to the main environmental variable. We conclude that the approach of complex networks is a promising tool for environmental diagnostic.
Abstract: Encoded information based on synchronization of coupled chaotic Nd:YAG lasers in master-slave configuration is numerically studied. Encoding, transmission, and decoding of information in optical chaotic communication with a single channel is presented. We analyze the robustness of the encrypted audio transmission in a channel noise. In order to illustrate this synchronization robustness, we present two cases of study: synchronization and transmission with a single channel without and with noise in the channel.
Abstract: Complex networks have been intensively studied across
many fields, especially in Internet technology, biological engineering,
and nonlinear science. Software is built up out of many interacting
components at various levels of granularity, such as functions, classes,
and packages, representing another important class of complex networks.
It can also be studied using complex network theory. Over the
last decade, many papers on the interdisciplinary research between
software engineering and complex networks have been published.
It provides a different dimension to our understanding of software
and also is very useful for the design and development of software
systems. This paper will explore how to use the complex network
theory to analyze software structure, and briefly review the main
advances in corresponding aspects.
Abstract: The Beijing road traffic system, as a typical huge
urban traffic system, provides a platform for analyzing the complex
characteristics and the evolving mechanisms of urban traffic systems.
Based on dynamic network theory, we construct the dynamic model
of the Beijing road traffic system in which the dynamical properties
are described completely. Furthermore, we come into the conclusion
that urban traffic systems can be viewed as static networks, stochastic
networks and complex networks at different system phases by
analyzing the structural randomness. As well as, we demonstrate the
evolving process of the Beijing road traffic network based on real
traffic data, validate the stochastic characteristics and the scale-free
property of the network at different phases
Abstract: Grid networks provide the ability to perform higher throughput computing by taking advantage of many networked computer-s resources to solve large-scale computation problems. As the popularity of the Grid networks has increased, there is a need to efficiently distribute the load among the resources accessible on the network. In this paper, we present a stochastic network system that gives a distributed load-balancing scheme by generating almost regular networks. This network system is self-organized and depends only on local information for load distribution and resource discovery. The in-degree of each node is refers to its free resources, and job assignment and resource discovery processes required for load balancing is accomplished by using fitted random sampling. Simulation results show that the generated network system provides an effective, scalable, and reliable load-balancing scheme for the distributed resources accessible on Grid networks.
Abstract: Mass-mail type worms have threatened to become a large problem for the Internet. Although many researchers have analyzed such worms, there are few studies that consider worm propagation via mailing lists. In this paper, we present a mass-mailing type worm propagation model including the mailing list effect on the propagation. We study its propagation by simulation with a real e¬mail social network model. We show that the impact of the mailing list on the mass-mail worm propagation is significant, even if the mailing list is not large.
Abstract: A new class of percolation model in complex networks,
in which nodes are characterized by hidden variables reflecting the
properties of nodes and the occupied probability of each link is
determined by the hidden variables of the end nodes, is studied
in this paper. By the mean field theory, the analytical expressions
for the phase of percolation transition is deduced. It is determined
by the distribution of the hidden variables for the nodes and the
occupied probability between pairs of them. Moreover, the analytical
expressions obtained are checked by means of numerical simulations
on a particular model. Besides, the general model can be applied
to describe and control practical diffusion models, such as disease
diffusion model, scientists cooperation networks, and so on.