Search

Nuclear reaction rate λ is a significant factor in processes of nucleosyntheses. A multi-layer directed-weighted nuclear reaction network, in which the reaction rate is taken as the weight, and neutron, proton, 4He and the remainder nuclei as the criteria for different reaction layers, is for the first time built based on all thermonuclear reactions in the JINA REACLIB database. Our results show that with the increase in the stellar temperature T9, the distribution of nuclear reaction rates on the R-layer network demonstrates a transition from unimodal to bimodal distributions. Nuclei on the R-layer in the region of λ = [1,2.5 × 101] have a more complicated out-going degree distribution than that in the region of λ = [1011,1013], and the number of involved nuclei at T9 = 1 is very different from the one at T9 = 3. The redundant nuclei in the region of λ = [1, 2.5 × 101] at T9 = 3 prefer (γ, p) and (γ,α) reactions to the ones at T9 = 1, which produce nuclei around the β stable line. This work offers a novel way to the big-data analysis on the nuclear reaction network at stellar temperatures. [ABSTRACT FROM AUTHOR]

Network transmission capacity is an important guarantee for the normal operation of the network. The effective routing strategy avoids the use of nodes with large degree value, which leads to low utilization of nodes and failure to consider the priorities of different packets. On this basis, a routing algorithm based on packet source node classification is proposed. This strategy introduces an adjustable parameter. By adjusting this parameter, the data packets generated at the important nodes are transferred to the nodes with higher degree, which is to say they can reach the destination faster. The data packets generated at the sub-important nodes are transmitted by nodes with smaller degrees, thus reaching the destination relatively slowly. The routing strategy is evaluated in terms of order parameters, average routing time and node utilization. Compared with nonclassified routing and randomly classified routing strategy, the network transmission capacity was increased by 19% and 38%, respectively. Each node in the network was used more evenly. At the same time, the network transmission capacity under different parameters is analyzed theoretically through a series of derivations. In order to explore the performance of routing strategy in actual networks, this paper selects the actual network of web-EPA for simulation. The experimental results show that the proposed routing strategy is 7% and 17% higher than the nonclassified routing and random classified routing, respectively. [ABSTRACT FROM AUTHOR]

Online Social Networks generate a prodigious wealth of real-time information at an incessant rate. In this paper we study the empirical data that crawled from Twitter to describe the topology and information spreading dynamics of Online Social Networks. We propose a measurement with three measures to state the efforts of users on Twitter to get their information spreading, based on the unique mechanisms for information retransmission on Twitter. It is noticed that small fraction of users with special performance on participation can gain great influence, while most other users play a role as middleware during the information propagation. Thus a community analysis is performed and four categories of users are found with different kinds of participation that cause the information dissemination dynamics. These suggest that exiting topological measures alone may reflect little about the influence of individuals and provide new insights for information spreading. [ABSTRACT FROM AUTHOR]

Abstract: We propose a spatial weighted network model based on the optimal expected traffic. The expected traffic represents the prediction of the flow created by two vertices and is calculated by the improved gravity equation. The model maximizes the total expected traffic of the network. By changing two parameters which control the fitness and the geographical constraints, the model can vary its topology and give rise to a variety of statistical properties observed in the real-world network. Notably, our study shows that a linear and a nonlinear strength–degree correlation can emerge when considering and neglecting the “transport effect”, respectively. [Copyright &y& Elsevier]

Abstract: The physics information of four specific airline flight networks in European Continent, namely the Austrian airline, the British airline, the France–Holland airline and the Lufthhansa airline, was quantitatively analyzed by the concepts of a complex network. It displays some features of small-world networks, namely a large clustering coefficient and small average shortest-path length for these specific airline networks. The degree distributions for the small degree branch reveal power law behavior with an exponent value of 2–3 for the Austrian and the British flight networks, and that of 1–2 for the France–Holland and the Lufthhansa airline flight networks. So the studied four airlines are sorted into two classes according to the topology structure. Similarly, the flight weight distributions show two kinds of different decay behavior with the flight weight: one for the Austrian and the British airlines and another for the France–Holland airline and the Lufthhansa airlines. In addition, the degree–degree correlation analysis shows that the network has disassortative behavior for all the value of degree , and this phenomenon is different from the international airline network and US airline network. Analysis of the clustering coefficient () versus , indicates that the flight networks of the Austrian Airline and the British Airline reveal a hierarchical organization for all airports, however, the France–Holland Airline and the Lufthhansa Airline show a hierarchical organization mostly for larger airports. The correlation of node strength () and degree is also analyzed, and a power-law fit can roughly fit all data of these four airline companies. Furthermore, we mention seasonal changes and holidays may cause the flight network to form a different topology. An example of the Austrian Airline during Christmas was studied and analyzed. [Copyright &y& Elsevier]

The traditional Gibrat's hypotheses were once used to model the topological fluctuations of Internet. Although it seems to reproduce the scaling relation of Internet's degree distribution, the detailed micro-dynamics have never been empirically validated. Here, we analyze the distribution of degree growth rates of the Internet for various time scales. We find that in contrast to the traditional Gibrat's assumptions, none of the degree growth rates are normally distributed, but behaves as an exponential decrease on its body and a power-law decay on its tail. Moreover, the observed growth rate distribution turns out independent of the initial degree when the time interval enlarges to a year. Our observations do not consist with the traditional Gibrat law model and suggest a more complex fluctuation mechanism underlying the evolution of Internet. [ABSTRACT FROM AUTHOR]

Abstract: The distribution of facilities is closely related to our social economic activities. Recent studies have reported a scaling relation between population and facility density, with the exponent depending on the type of facility. In this paper, we show that generally this exponent is not universal for a specific type of facility. Instead, by using Chinese data, we find that it increases with per capita gross domestic product (GDP). Thus our observed scaling law is actually a mixture of several multi-scaling relations. This result indicates that facilities may change their public or commercial attributes according to the outside environment. We argue that this phenomenon results from an unbalanced regional economic level, and suggest a modification of a previous model by introducing the consuming capacity. The modified model reproduces most of our observed properties. [Copyright &y& Elsevier]

This paper analyzed two types of empirical treelike transport network: natural evolution clonal plant network and artificial construction metro network. It constructed the models in space L and space P respectively. Although these two types of networks construction for different purposes, they exhibited similar topology in space L and space P models. Compared these two networks with random branching tree, it was found that they deviated from the predicted random branching tree in the same manner and extent. [ABSTRACT FROM AUTHOR]