Communities and dynamical processes in a complex software network

Complex technological networks represent a growing challenge to support and maintain as their number of elements become higher and their interdependencies more involved. On the other hand, for networks that grow in a decentralized manner, it is possible to observe certain patterns in their overall structure that may be taken into account for a more tractable analysis. An example of such a pattern is the spontaneous formation of communities or modules. An important question regarding the detection of communities is if these are really representative of any internal network feature. In this work, we explore the community structure of a real complex software network, and correlate this modularity information with the internal dynamical processes that the network is designed to support. Our results show that the dependence between community structure and internal dynamical processes is remarkable, supporting the fact that a community division of this complex network is helpful in the assessment of the underlying dynamical structure, and thus is a useful tool to achieve a simpler representation of the complexity of the network.     

A self-consistent approach to measure preferential attachment in networks and its application to an inherent structure network

Preferential attachment is one possible way to obtain a scale-free network. We develop a self-consistent method to determine whether preferential attachment occurs during the growth of a network, and to extract the preferential attachment rule using time-dependent data. Model networks are grown with known preferential attachment rules to test the method, which is seen to be robust. The method is then applied to a scale-free inherent structure (IS) network, which represents the connections between minima via transition states on a potential energy landscape. Even though this network is static, we can examine the growth of the network as a function of a threshold energy (rather than time), where only those transition states with energies lower than the threshold energy contribute to the network. For these networks we are able to detect the presence of preferential attachment, and this helps to explain the ubiquity of funnels on potential energy landscapes. However, the scale-free degree distribution shows some differences from that of a model network grown using the obtained preferential attachment rules, implying that other factors are also important in the growth process.     

Compatibility as underlying mechanism behind the evolution of networks

In this work we propose a dynamic network model based on an evolutive rule called compatibility. The presence of common topological properties observed in two real complex networks are well reproduced by the proposed model. These results suggest that the compatibility between nodes is sufficient to generate the complexity observed in real systems even considering that the new added nodes do not have the ability to know global information of the system.     

Network growth with preferential attachment for high indegree and low outdegree

We study the growth of a directed transportation network, such as a food web, in which links carry resources. We propose a growth process in which new nodes (or species) preferentially attach to existing nodes with high indegree (in food-web language, number of prey) and low outdegree (or number of predators). This scheme, which we call inverse preferential attachment, is intended to maximize the amount of resources available to each new node. We show that the outdegree (predator) distribution decays at least exponentially fast for large outdegree and is continuously tunable between an exponential distribution and a delta function. The indegree (prey) distribution is poissonian in the large-network limit.     

Local preferential attachment model for hierarchical networks

In real-life networks, incomers may only connect to a few others in a local area for their limited information, and individuals in a local area are likely to have close relations. Accordingly, we propose a local preferential attachment model. Here, a local-area-network stands for a node and all its neighbors, and the new nodes perform nonlinear preferential attachment, , in local areas. The stable degree distribution and clustering-degree correlations are analytically obtained. With the increasing of α, the clustering coefficient increases, while assortativity decreases from positive to negative. In addition, by adjusting the parameter α, the model can generate different kinds of degree distribution, from exponential to power-law. The hierarchical organization, independent of α, is the most significant character of this model.     

Time evolution of the degree distribution of model A of random attachment growing networks

For random growing networks, Barabás and Albert proposed a kind of model in Barabás et al. [Physica A 272 (1999) 173], i.e. model A. In this paper, for model A, we give the differential format of master equation of degree distribution and obtain its analytical solution. The obtained result P(k, t) is the time evolution of degree distribution. P(k, t) is composed of two terms. At given finite time, one term decays exponentially, the other reflects size effect. At infinite time, the degree distribution is the same as that of Barabás and Albert. In this paper, we also discuss the normalization of degree distribution P(k, t) in detail.     

A PC compatible Brazilian software for obtaining thermal histories using apatite fission track analysis

In this work a software developed in the Instituto de Física Gleb Wataghin, IFGW, UNICAMP, Campinas, SP, Brazil for obtaining thermal histories using apatite fission track analysis is presented. This software works in Microsoft-Windows environment. It will be freely disposable in the web site of the Departamento de Raios Cósmicos, IFGW, UNICAMP. Thermal histories obtained through this software are compared with those deduced using Monte Trax the software compatible with Apple Macintosh developed by Gallagher.     

A complex network model for seismicity based on mutual information

Seismicity is the product of the interaction between the different parts of the lithosphere. Here, we model each part of the Earth as a cell that is constantly communicating its state to its environment. As a neuron is stimulated and produces an output, the different parts of the lithosphere are constantly stimulated by both other cells and the ductile part of the lithosphere, and produce an output in the form of a stress transfer or an earthquake. This output depends on the properties of each part of the Earth’s crust and the magnitude of the inputs. In this study, we propose an approach to the quantification of this communication, with the aid of the Information Theory, and model seismicity as a Complex Network. We have used data from California, and this new approach gives a better understanding of the processes involved in the formation of seismic patterns in that region.     

Development of TUSCAT: A software for light scattering studies on spherical, spheroidal and cylindrical particles

An integrated software package TUSCAT (Tezpur University SCATtering Software) incorporated with a graphical user interface (GUI) was developed for modeling electromagnetic scattering from small particles and also to yield characteristic properties of the scattering particles from experimental data. Its interactive features enable the user to observe the changes in output scattering properties in real time. In addition to its ease of use, it has high computational accuracy, efficiency, reliability and adaptability.     

A detailed chemical kinetic reaction mechanism for oxidation of four small alkyl esters in laminar premixed flames

A detailed chemical kinetic reaction mechanism has been developed for a group of four small alkyl ester fuels, consisting of methyl formate, methyl acetate, ethyl formate, and ethyl acetate. This mechanism is validated by comparisons between computed results and recently measured intermediate species mole fractions in fuel-rich, low-pressure, premixed laminar flames. The model development employs a principle of similarity of functional groups in constraining the H atom abstraction and unimolecular decomposition reactions for each of these fuels. As a result, the reaction mechanism and formalism for mechanism development are suitable for extension to larger oxygenated hydrocarbon fuels, together with an improved kinetic understanding of the structure and chemical kinetics of alkyl ester fuels that can be extended to biodiesel fuels. Variations in concentrations of intermediate species levels in these flames are traced to differences in the molecular structure of the fuel molecules.     

A new flexible and enhancing bandwidth utilization burst dropping technique for an OBS network

Optical burst switching (OBS) has been proposed as a competitive hybrid switching technology to support the next generation optical Internet. However, due to their one-way resource reservation mechanism, OBS networks experience high bursts (thus packets) loss rate. In OBS networks, the contention is resolved either by dropping one of the contending bursts or more efficiently by dropping from one of the contending bursts only the parts that overlap with the other bursts. In both situations, only one data source will suffer the data loss in favor to the other. In this paper, a new burst flexible and enhancing bandwidth utilization burst dropping technique has been proposed for contention resolution in optical burst switched networks. When contention occurs, any part of a contending burst could be dropped, instead of only the head or tail of bursts. The proposed dropping scheme makes bandwidth utilization more efficient and flexible. Simulation results show that the proposed dropping scheme performs better than existing burst dropping schemes.     

一种适于光学实现的非线性神经网络模型及其蒙特卡洛学习算法

本文用计算机仿真研究了一种适于光学实现的非线性神经网络模型的存储客量α_c和寻址能力,提出了一个改进其触突互联矩阵的蒙特卡洛学习算法.数值研究表明,经过学习修正后的神经网络模型的寻址能力及存储容量都有较大的改进.     

一种新的自组织聚类网络及其在数据融合目标分类中的应用

介绍了一种新的自组织聚类人工神经网络(DIGNET)模型和网络的非监督学习算法。针对数据融合和目标识别的特点,提出了基于DIGNET的决策层数据融合目标分类方法。利用仿真数据研究了DIGNET和自组织特征映射网络(SOFM)的聚类性能以及基于DIGNET的决策层数据融合结构,实验结果表明DIGNET较SOFM正确分类率高、抗噪能力好;基于DIGNET的决策层数据融合能够有效地实现融合识别。将该数据融合方法应用于前视红外(FLIR)和可见光摄像机目标跟踪系统,结果表明该方法是可行的。