Maximum entropy networks are more controllable than preferential attachment networks

A maximum entropy (ME) method to generate typical scale-free networks has been recently introduced. We investigate the controllability of ME networks and Barabási–Albert preferential attachment networks. Our experimental results show that ME networks are significantly more easily controlled than BA networks of the same size and the same degree distribution. Moreover, the control profiles are used to provide insight into control properties of both classes of network. We identify and classify the driver nodes and analyze the connectivity of their neighbors. We find that driver nodes in ME networks have fewer mutual neighbors and that their neighbors have lower average degree. We conclude that the properties of the neighbors of driver node sensitively affect the network controllability. Hence, subtle and important structural differences exist between BA networks and typical scale-free networks of the same degree distribution.     

Symmetry-based structure entropy of complex networks

Precisely quantifying the heterogeneity or disorder of network systems is important and desired in studies of behaviors and functions of network systems. Although various degree-based entropies have been available to measure the heterogeneity of real networks, heterogeneity implicated in the structures of networks can not be precisely quantified yet. Hence, we propose a new structure entropy based on automorphism partition. Analysis of extreme cases shows that entropy based on automorphism partition can quantify the structural heterogeneity of networks more precisely than degree-based entropies. We also summarized symmetry and heterogeneity statistics of many real networks, finding that real networks are more heterogeneous in the view of automorphism partition than what have been depicted under the measurement of degree-based entropies; and that structural heterogeneity is strongly negatively correlated to symmetry of real networks.     

Intermittency from maximum entropy distribution

A maximum entropy distribution has been formulated in which the imposed constraint contains a stochastic (rather than a deterministic) variable. The distribution depends on the observational bin size through the smoothing of population by intrabin averaging. Moments of fluctuations calculated with this distribution give bin-size dependences (intermittency exponents) that agree reasonably with those obtained from the size dependence in nuclear multifragmentation. The exponents depend on the spread of the stochastic mechanism (supposed to be a cascading, multiplicative process) and on the magnitude of the constraint imposed. An information-theoretic interpretation is provided for the relation between statistical and mechanism-induced (dynamic) fluctuations.     

Synchronization in scale free networks with degree correlation

In this paper we study a model of synchronization process on scale free networks with degree-degree correlations. This model was already studied on this kind of networks without correlations by Pastore y Piontti et al. [A.L. Pastore y Piontti, P.A. Macri, L.A. Braunstein, Phys. Rev. E 76 (2007) 046117]. Here, we study the effects of the degree-degree correlation on the behavior of the load fluctuations W_s in the steady state. We found that for assortative networks there exist a specific correlation where the system is optimally synchronized. In addition, we found that close to this optimal value the fluctuations does not depend on the system size and therefore the system becomes fully scalable. This result could be very important for some technological applications. On the other hand, far from the optimal correlation, W_s scales logarithmically with the system size.     

The degree distribution of fixed act-size collaboration networks

In this paper, we investigate a special evolving model of collaboration networks, where the act-size is fixed. Based on the first-passage probability of Markov chain theory, this paper provides a rigorous proof for the existence of a limiting degree distribution of this model and proves that the degree distribution obeys the power-law form with the exponent adjustable between 2 and 3.     

信息熵、玻尔兹曼熵以及克劳修斯熵之间的关系--兼论玻尔兹曼熵和克劳修斯熵是否等价

论述了信息熵、玻尔兹曼熵以及克劳修斯熵之间的关系;由不涉及具体系统的方法从玻尔兹曼关系、信息熵推导出了克劳修斯熵的表达式;指出玻尔兹曼熵与克劳修斯熵不是等价关系,而是玻尔兹曼熵包含克劳修斯熵,信息熵又包含玻尔兹曼熵。     

Field-induced structural transition and the related magnetic entropy change in Ni43Mn43Co3Sn11 alloy

Magnetic properties and magnetic entropy change ΔS were investigated in Heusler alloy Ni₄₃Mn₄₃Co₃Sn₁₁. With decreasing temperature this alloy undergoes a martensitic structural transition at T_M=188 K. The incorporation of Co atoms enhances ferromagnetic exchange for parent phases. Austenitic phase with cubic structure shows strong ferromagnetic behaviors with Curie temperature T_C^A at 346 K, while martensitic phase shows weak ferromagnetic properties. An external magnetic field can shift T_M to a lower temperature at a rate of 4.4 K/T, and a field-induced structural transition from martensitic to austenitic state takes place at temperatures near but below T_M. As a result, a great magnetic entropy change with positive sign appears. The size of ΔS reaches 33 J/kg K under 5 T magnetic field. More important is that the ΔS displays a table-like peak under 5 T, which is favorable for Ericsson-type refrigerators.     

An improved model for structural vulnerability analysis of power networks

Electric power networks have been studied as a typical example of real-world complex networks. Traditional models for structural vulnerability analysis appear to be all based on physical topological structure. In this paper, we depict a typical power network as a weighted graph based on electrical topology by introducing its bus admittance matrix, which embodies the important characteristics of power networks in a much more realistic structure. Furthermore, the numerical simulation for both the traditional dynamical model and the proposed electrical topological model are investigated based on the IEEE 300 bus system respectively. The comparison demonstrates that the improved model is more precise and highly efficient for the analysis of structural vulnerability of power networks.     

Entanglement entropy at infinite-randomness fixed points in higher dimensions

The entanglement entropy of the two-dimensional random transverse Ising model is studied with a numerical implementation of the strong-disorder renormalization group. The asymptotic behavior of the entropy per surface area diverges at, and only at, the quantum phase transition that is governed by an infinite-randomness fixed point. Here we identify a double-logarithmic multiplicative correction to the area law for the entanglement entropy. This contrasts with the pure area law valid at the infinite-randomness fixed point in the diluted transverse Ising model in higher dimensions.     

Detecting communities by the core-vertex and intimate degree in complex networks

In this paper, we present a new approach to extract communities in the complex networks with considerable accuracy. We introduce the core-vertex and the intimate degree between the community and its neighboring vertices. First, we find the core-vertices as the initial community. These core-vertices are then expanded using intimate degree function during extracting community structure from the given network. In addition, our algorithm successfully finds common nodes between communities. Experimental results using some real-world networks data shows that the performance of our algorithm is satisfactory.     

Network analysis of time series under the constraint of fixed nearest neighbors

In this paper, we carried out network analysis for typical time series, such as periodic signals, chaotic maps, Gaussian white noise, and fractal Brownian motions. By reconstructing the phase space for a given time series, we can generate a network under the constraint of fixed nearest neighbors. The mapped networks are then analyzed from both the statistical properties, such as degree distribution, clustering coefficient, betweenness, etc, as well as the local topological structures, i.e., network motifs. It is shown that time series of different nature can be distinguished from these two aspects of the constructed networks.     

Scale free distribution in an analytical approach

In order to explain the scale free feature of complex networks, we introduce an analytical approach for investigating the degree distribution. We represent the degree distribution by the probability density function, where the correspondence between them is given approximately by the transformation from discrete number, degree, to a continuous variable. We find that arbitrary representations of the degree distribution as the probability density function are reduced to a specific form which obeys scale free. Our result provides one explanation for the ubiquity of scale free networks.     

Topological permutation entropy

Permutation entropy quantifies the diversity of possible ordering of the successively observed values a random or deterministic system can take, just as Shannon entropy quantifies the diversity of the values themselves. When the observable or state variable has a natural order relation, making permutation entropy possible to compute, then the asymptotic rate of growth in permutation entropy with word length forms an alternative means of describing the intrinsic entropy rate of a source. Herein, extending a previous result on metric entropy rate, we show that the topological permutation entropy rate for expansive maps equals the conventional topological entropy rate familiar from symbolic dynamics. This result is not limited to one-dimensional maps.     

The number of spanning trees of an infinite family of outerplanar,small-world and self-similar graphs

In this paper we give an exact analytical expression for the number of spanning trees of an infinite family of outerplanar, small-world and self-similar graphs. This number is an important graph invariant related to different topological and dynamic properties of the graph, such as its reliability, synchronization capability and diffusion properties. The calculation of the number of spanning trees is a demanding and difficult task, in particular for large graphs, and thus there is much interest in obtaining closed expressions for relevant infinite graph families. We have also calculated the spanning tree entropy of the graphs which we have compared with those for graphs with the same average degree.     

Nonadditive Tsallis entropy applied to the Earth’s climate

The concepts of nonextensive statistics, which has been applied in the study of complex systems, are used to analyze past records of the Earth’s climate. The fluctuations within the record of deuterium content (hence temperature) in the last glacial period appear to follow a q-Gaussian distribution. Analyses of the time-dependent nonadditive entropy indicate transitions between different complexity levels in the data prior to the abrupt change in the system dynamics at the end of the last glaciation. Different fluctuation regimens are evidenced through wavelets analysis. It is also suggested that time-dependent entropy analysis could be useful for indicating the approach to a critical transition of the Earth’s climate for which theoretical models are in many cases not available.     

Normalized entropy of rank distribution： a novel measure of heterogeneity of complex networks

Many unique properties of complex networks result from heterogeneity. The measure and analysis of heterogeneity are important and desirable to the research of the properties and functions of complex networks. In this paper, the rank distribution is proposed as a new statistic feature of complex networks. Based on the rank distribution, a novel measure of the heterogeneity called a normalized entropy of rank distribution (NERD) is proposed. The NERD accords with the normal meaning of heterogeneity within the context of complex networks compared with conventional measures. The heterogeneity of scale-free networks is studied using the NERD. It is shown that scale-free networks become more heterogeneous as the scaling exponent decreases and the NERD of scale-free networks is independent of the number of vertices, which indicates that the NERD is a suitable and effective measure of heterogeneity for networks with different sizes.     

Detecting local community structures in complex networks based on local degree central nodes

Detecting local communities in real-world graphs such as large social networks, web graphs, and biological networks has received a great deal of attention because obtaining complete information from a large network is still difficult and unrealistic nowadays. In this paper, we define the term local degree central node whose degree is greater than or equal to the degree of its neighbor nodes. A new method based on the local degree central node to detect the local community is proposed. In our method, the local community is not discovered from the given starting node, but from the local degree central node that is associated with the given starting node. Experiments show that the local central nodes are key nodes of communities in complex networks and the local communities detected by our method have high accuracy. Our algorithm can discover local communities accurately for more nodes and is an effective method to explore community structures of large networks.     

测度熵计算中映射不变分布的应用

构造计算测度熵(MetricEntropy)的马尔可夫划分(MarkovPartition)时,对于非线性映射采取等似然假设,影响精度。提出用计算映射的不变分布来进行改进;对逻辑映射导出映射不变分布的算式及迭代格式,进行了测度熵的计算;计算表明,迭代次数不多时即能得出较好结果。     

简易广义合作网络度分布的稳定性

本文对简易广义合作网络的三类特殊情形（择优连接、随机连接、混合连接）进行了研究. 基于马氏链理论, 给出它们度分布稳定性存在的严格证明, 并且得到相应网络度分布和度指数的精确表达式. 特别地, 对于混合连接情况, 说明在连线方式中只要存在择优成分, 网络度分布就服从幂律分布, 即所得网络为无标度网络. 关键词： 简易广义合作网络 无标度网络 马氏链 度分布     

熵的一种通俗教法

熵如力、能量和动量一样是物理学中一个重要概念,若能用一种通俗易懂的方法设计熵的教学,对文科物理的教学有重要意义.为此本文提出了一种通俗的熵的教法,这一教法不需要学生学习热力学第二定律也可以建立熵的概念.具体教学设计如下:通过日常生活例子引入熵的概念(也就是玻尔兹曼熵),设计两个例子让学生会计算熵,通过具体问题的讨论让学生充分理解熵的意义,通过一个实例由玻尔兹曼熵引入克劳修斯熵公式,设计一个演示实验强化教学效果,将熵与环境保护联系起来融入人文情怀,最后还强调了熵计算的不同层次.教学设计完全采用基于问题学习(PBL)的教学模式.