280,925 research outputs found
Characteristics of Real Futures Trading Networks
Futures trading is the core of futures business, and it is considered as one
of the typical complex systems. To investigate the complexity of futures
trading, we employ the analytical method of complex networks. First, we use
real trading records from the Shanghai Futures Exchange to construct futures
trading networks, in which nodes are trading participants, and two nodes have a
common edge if the two corresponding investors appear simultaneously in at
least one trading record as a purchaser and a seller respectively. Then, we
conduct a comprehensive statistical analysis on the constructed futures trading
networks. Empirical results show that the futures trading networks exhibit
features such as scale-free behavior with interesting odd-even-degree
divergence in low-degree regions, small-world effect, hierarchical
organization, power-law betweenness distribution, disassortative mixing, and
shrinkage of both the average path length and the diameter as network size
increases. To the best of our knowledge, this is the first work that uses real
data to study futures trading networks, and we argue that the research results
can shed light on the nature of real futures business.Comment: 18 pages, 9 figures. Final version published in Physica
Power Grid Network Evolutions for Local Energy Trading
The shift towards an energy Grid dominated by prosumers (consumers and
producers of energy) will inevitably have repercussions on the distribution
infrastructure. Today it is a hierarchical one designed to deliver energy from
large scale facilities to end-users. Tomorrow it will be a capillary
infrastructure at the medium and Low Voltage levels that will support local
energy trading among prosumers. In our previous work, we analyzed the Dutch
Power Grid and made an initial analysis of the economic impact topological
properties have on decentralized energy trading. In this paper, we go one step
further and investigate how different networks topologies and growth models
facilitate the emergence of a decentralized market. In particular, we show how
the connectivity plays an important role in improving the properties of
reliability and path-cost reduction. From the economic point of view, we
estimate how the topological evolutions facilitate local electricity
distribution, taking into account the main cost ingredient required for
increasing network connectivity, i.e., the price of cabling
Hybrid integration of multilayer perceptrons and parametric models for reliability forecasting in the smart grid
The reliable power system operation is a major goal for electric utilities, which requires the accurate reliability forecasting to minimize the duration of power interruptions. Since weather conditions are usually the leading causes for power interruptions in the smart grid, especially for its distribution networks, this paper comprehensively investigates the combined effect of various weather parameters on the reliability performance of distribution networks. Specially, a multilayer perceptron (MLP) based framework is proposed to forecast the daily numbers of sustained and momentary power interruptions in one distribution management area using time series of common weather data. First, the parametric regression models are implemented to analyze the relationship between the daily numbers of power interruptions and various common weather parameters, such as temperature, precipitation, air pressure, wind speed, and lightning. The selected weather parameters and corresponding parametric models are then integrated as inputs to formulate a MLP neural network model to predict the daily numbers of power interruptions. A modified extreme learning machine (ELM) based hierarchical learning algorithm is introduced for training the formulated model using realtime reliability data from an electric utility in Florida and common weather data from National Climatic Data Center (NCDC). In addition, the sensitivity analysis is implemented to determine the various impacts of different weather parameters on the daily numbers of power interruptions
Quantum Google in a Complex Network
We investigate the behavior of the recently proposed quantum Google
algorithm, or quantum PageRank, in large complex networks. Applying the quantum
algorithm to a part of the real World Wide Web, we find that the algorithm is
able to univocally reveal the underlying scale-free topology of the network and
to clearly identify and order the most relevant nodes (hubs) of the graph
according to their importance in the network structure. Moreover, our results
show that the quantum PageRank algorithm generically leads to changes in the
hierarchy of nodes. In addition, as compared to its classical counterpart, the
quantum algorithm is capable to clearly highlight the structure of secondary
hubs of the network, and to partially resolve the degeneracy in importance of
the low lying part of the list of rankings, which represents a typical
shortcoming of the classical PageRank algorithm. Complementary to this study,
our analysis shows that the algorithm is able to clearly distinguish scale-free
networks from other widespread and important classes of complex networks, such
as Erd\H{o}s-R\'enyi networks and hierarchical graphs. We show that the ranking
capabilities of the quantum PageRank algorithm are related to an increased
stability with respect to a variation of the damping parameter that
appears in the Google algorithm, and to a more clearly pronounced power-law
behavior in the distribution of importance among the nodes, as compared to the
classical algorithm. Finally, we study to which extent the increased
sensitivity of the quantum algorithm persists under coordinated attacks of the
most important nodes in scale-free and Erd\H{o}s-R\'enyi random graphs
On scale-free and poly-scale behaviors of random hierarchical network
In this paper the question about statistical properties of
block--hierarchical random matrices is raised for the first time in connection
with structural characteristics of random hierarchical networks obtained by
mipmapping procedure. In particular, we compute numerically the spectral
density of large random adjacency matrices defined by a hierarchy of the
Bernoulli distributions on matrix elements, where
depends on hierarchy level as (). For the spectral density we clearly see the free--scale
behavior. We show also that for the Gaussian distributions on matrix elements
with zero mean and variances , the tail of the
spectral density, , behaves as for and , while for
the power--law behavior is terminated. We also find that the vertex
degree distribution of such hierarchical networks has a poly--scale fractal
behavior extended to a very broad range of scales.Comment: 11 pages, 6 figures (paper is substantially revised
- …