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Über dieses Buch

I was invited to join the Organizing Committee of the First International Conference on Complex Sciences: Theory and Applications (Complex 2009) as its ninth member. At that moment, eight distinguished colleagues, General Co-chairs Eugene Stanley and Gaoxi Xiao, Technical Co-chairs J·nos Kertész and Bing-Hong Wang, Local Co-chairs Hengshan Wang and Hong-An Che, Publicity Team Shi Xiao and Yubo Wang, had spent hundreds of hours pushing the conference half way to its birth. Ever since then, I have been amazed to see hundreds of papers flooding in, reviewed and commented on by the TPC members. Finally, more than 200 contributions were - lected for the proceedings currently in your hands. They include about 200 papers from the main conference (selected from more than 320 submissions) and about 33 papers from the five collated workshops: Complexity Theory of Art and Music (COART) Causality in Complex Systems (ComplexCCS) Complex Engineering Networks (ComplexEN) Modeling and Analysis of Human Dynamics (MANDYN) Social Physics and its Applications (SPA) Complex sciences are expanding their colonies at such a dazzling speed that it - comes literally impossible for any conference to cover all the frontiers.

Inhaltsverzeichnis

Frontmatter

Part II

Frontmatter

Chaotic and Hyperchaotic Attractors in Time-Delayed Neural Networks

It is well known that complex dynamic behaviors exist in time-delayed neural networks. Infinite positive Lyapunov exponents can be found in time-delayed chaotic systems since the dimension of such systems is infinite. This paper presents an infinite-dimension hyperchaotic time-delayed neuron system with sinusoidal activation function. The hyperchaotic neuron system is studied by Lyapunov exponent, phase diagram, Poincare section and power spectrum. Numerical simulations show that the new system’s behavior can be convergent, periodic, chaotic and hyperchaotic when the time-delay parameter varies.

Dong Zhang, Jian Xu

Channel Estimation and ISI/ICI Cancellation for MIMO-OFDM Systems with Insufficient Cyclic Prefix

In multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, the multipath components whose delays exceed cyclic prefix (CP) cause inter-symbol interference (ISI) and inter-carrier interference (ICI), which may degrade system performance severely. In this paper, we propose a joint channel estimation and ISI/ICI cancellation scheme in which a limited CP is used in a trade-off against high-rate performance in MIMO-OFDM systems. A channel estimation scheme based on the criterion of Expectation-Maximization (EM) algorithm can be proposed through the use of a training symbol. The EM algorithm uses an iterative procedure to estimate channel parameters and can estimate channel impulse response (CIR) accurately enough to mitigate ISI/ICI influences. Through the accurate CIR estimation, an efficient method has been developed to counteract ISI/ICI influences in signal detection in the case where the inserted CP length is less than the CIR length. Simulation results show that the proposed method can significantly enhance the overall MIMO-OFDM system performance after only a few iterations.

Yi-Jen Chiu, Chien-Sheng Chen, Ting-Wei Chang

Capturing Internet Traffic Dynamics through Graph Distances

Studies of the Internet have typically focused either on the routing system, i.e. the paths chosen to reach a given destination, or on the evolution of traffic on a physical link. In this paper, we combine routing and traffic, and study for the first time the evolution of the traffic on the Internet topology. We rely on the traffic and routing data of a large transit provider, spanning almost a month.

We compute distances between the traffic graph over small and large timescales. We find that the global traffic distribution on the AS graph largely differs from traffic observed at small timescales. However, variations between consecutive time periods are relatively limited, i.e. the topology spanned by the traffic from one time period to the next is small. This difference between local and global traffic distribution is found in the timescales at which traffic dynamics occurs on AS-level links. Small timescales, i.e. less than a few hours, do not account for a significant fraction of the traffic dynamics. Most of the traffic variability is concentrated at timescales of days. Models of Internet traffic on its topology should thus focus on capturing the long-term changes in the global traffic pattern.

Steve Uhlig, Bingjie Fu, Almerima Jamakovic

Cache Allocation in CDN: An Evolutionary Game Generalized Particle Model

Content distribution networks (CDNs) increasingly have been used to reduce the response times experienced by Internet users through placing surrogates close to the clients. This paper presents an object replacement approach based on an evolutionary game generalized particle model (G-GPM). We first propose a problem model for CDNs. The CDN model is then fit into a gravitational field. The origin servers and surrogates are regarded as two kinds of particles which are located in two force-fields. The cache allocation problem is thus transformed into the kinematics and dynamics of the particles in the annular and the round force-fields. The G-GPM approach is unique in four aspects: 1) direct viewing of individual and overall optimization; 2) parallel computing (lower time complexity); 3) multi-objective solution; and 4) being able to deal with some social interactions behaviors.

Xiang Feng, Francis C. M. Lau, Daqi Gao

Briefly Review of China High Technology Networks

We briefly review and summarize the results in research of China High Technology Networks(CHTN), which can be composed by three levels. A weighted framework is put forward and can be used to study some similar networks. According to our idea and method, the CHTN is constructed. Then the CHTN’s topological properties are investigated, including degree distribution, shortest path length, clustering coefficient, degree-degree correla-tion and community structure. The quantitative results can be helpful for promoting management and adjusting structures of the CHTN.

Yong Li, Jin-Qing Fang, Qiang Liu

Block & Comovement Effect of Stock Market in Financial Complex Network

In the work, we present a method to analyze block & comovement effect of stock market by finding out the community structure in the financial complex network. We choose the stocks from Shanghai and Shenzhen 300 Index as data source and convert them into the complex network in matrix format which is based on the measurements of correlation we proposed in this paper. The classical GN algorithm and the NetDraw tool are applied to obtain the modularity and draw all the community structures. The results of our work can offer not only the internal information about the capital flows in the stock market but also the prediction of variation and trend line of some stocks with delay-correlation.

Chongwei Du, Xiong Wang, Liyin Qiu

An Approach to Enhance Convergence Efficiency of Self-propelled Agent System

In this paper, we investigate a weighted self-propelled particles system, wherein each agent’s direction is determined by its spatial neighbors’ directions with exponential weights concerning the neighbor numbers. In order to describe the fact that some agent with more neighbors might have much larger influence on its neighbors, we introduce a scaling exponent of the neighbor number between 0 and ∞. As the exponent increases, i.e., the effect of weight becomes stronger, the network of agents becomes much easier to achieve direction consensus in our simulation. Especially, when the exponent equals to 1, the convergence efficiency is enhanced.

Jian-xi Gao, Zhuo Chen, Yun-ze Cai, Xiao-ming Xu

An Application on Merton Model in the Non-efficient Market

Merton Model is one of the famous credit risk models. This model presumes that the only source of uncertainty in equity prices is the firm’s net asset value .But the above market condition holds only when the market is efficient which is often been ignored in modern research. Another, the original Merton Model is based on assumptions that in the event of default absolute priority holds, renegotiation is not permitted , liquidation of the firm is costless and in the Merton Model and most of its modified version the default boundary is assumed to be constant which don’t correspond with the reality. So these can influence the level of predictive power of the model. In this paper, we have made some extensions on some of these assumptions underlying the original model. The model is virtually a modification of Merton’s model. In a non-efficient market, we use the stock data to analysis this model. The result shows that the modified model can evaluate the credit risk well in the non-efficient market.

Yanan Feng, Qingxian Xiao

A Novel Software Evolution Model Based on Software Networks

Many published papers analyzed the forming mechanisms and evolution laws of OO software systems from software reuse, software pattern, etc. There, however, have been fewer models so far merely built on the software components such as methods, classes, etc. and their interactions. In this paper, a novel Software Evolution Model based on Software Networks (called SEM-SN) is proposed. It uses software network at class level to represent software systems, and uses software network’s dynamical generating process to simulate activities in real software development process such as new classes’ dynamical creations and their dynamical interactions with already existing classes. It also introduces the concept of node/edge ageing to describe the decaying of classes with time. Empirical results on eight open-source Object-Oriented (OO) software systems demonstrate that SCM-SN roughly describes the evolution process of software systems and the emergence of their complex network characteristics.

Weifeng Pan, Bing Li, Yutao Ma, Jing Liu

A Novel Measurement of Structure Properties in Complex Networks

Traditional measurements provide an effective tool to study the complex large systems in the real world. These global quantities only analyze the general statistical properties and interconnectivity structure of the entire network. However the complicated interactions among the locals are indeed the origin to emergent complex behavior. So in this paper we present a new measurement to reveal the local structure properties - topology potential, which reflects the differential position of each node in the topology. It is flexible by adjusting the influence factor. We demonstrate our measurement in US politics books network. Experiments confirm that topology potential has inherently implied the traditional measurements to some extent.

Yanni Han, Jun Hu, Deyi Li, Shuqing Zhang

A New Genetic Algorithm for Community Detection

With the rapidly grown evidence that various systems in nature and society can be modeled as complex networks, community detection in networks becomes a hot research topic in many research fields. This paper proposes a new genetic algorithm for community detection. The algorithm uses the fundamental measure criterion modularity Q as the fitness function. A special locus-based adjacency encoding scheme is applied to represent the community partition. The encoding scheme is suitable for the community detection based on the reason that it determines the community number automatically and reduces the search space distinctly. In addition, the corresponding crossover and mutation operators are designed. The experiments in three aspects show that the algorithm is effective, efficient and steady.

Chuan Shi, Yi Wang, Bin Wu, Cha Zhong

A New Bio-inspired Approach to the Traveling Salesman Problem

The host-seeking behavior of mosquitoes is very interesting. In this paper, we propose a novel mosquito host-seeking algorithm (MHSA) as a new branch of biology-inspired algorithms for solving TSP problems. The MHSA is inspired by the host-seeking behavior of mosquitoes. We present the mathematical model, the algorithm, the motivation, and the biological model. The MHSA can work out the theoretical optimum solution, which is important and exciting, and we give the theoretical foundation and present experiment results that verify this fact.

Xiang Feng, Francis C. M. Lau, Daqi Gao

A More Strict Definition of Steady State Degree Distribution

Accurate definitions of related concepts are prerequisite for further understanding of evolving network. To be an important concept, steady state degree distribution has been widely used. However, as we find out, all current definitions have a common default from mathematics point of view. In this paper, we first point out the shortcoming of current definitions through a special type of evolving network, and then provide a more strict definition of steady state degree distribution from stochastic process point of view.

Xiaojun Zhang, Zheng He

A Max-Min Principle for Phyllotactic Patterns

An interesting phenomenon about phyllotaxis is the divergence angle between two consecutive primordia. In this paper, we consider a dynamic model based on Max-Min principle for generating 2D phyllotactic patterns studied in [2,5]. Under the hypothesis that the influence of the two predecessors is enough to fix the birth place of the new generated primordium, analysis and numerical experiments are conducted. We then propose a new measurement for evaluating the pattern uniformity (sparsity) of different divergence angles. It is found that the golden angle gives very good sparsity but there are other angles give even better sparsity under our proposed measurement.

Wai-Ki Ching, Yang Cong, Nam-Kiu Tsing

A Hybrid Ant-Colony Routing Algorithm for Mobile Ad-Hoc Networks

The dynamic nature of mobile ad hoc networks makes it difficult to consider a specific model for their topology which might change in a short period of time. Using the knowledge about the location of nodes, several relatively efficient position based routing algorithms have been proposed but almost all of them are sensitive to the network topology. Ant colony optimization based routing algorithms form another family of routing algorithms that usually converge to optimum routes. In our previous work we proposed POSANT, a position based ant colony routing algorithm for mobile ad-hoc networks. Although POSANT outperforms other routing algorithms in most cases, there are network topologies in which POSANT does not perform well. In this paper we introduce HybNet, a hybrid ant colony optimization based routing algorithm for mobile ad hoc networks which adapts itself to different network topologies. We carry out an empirical analysis of the performance of our algorithm and compare it with other routing algorithms. Our results show that HybNet almost always performs efficiently, even in some complex and variable network topologies.

Shahab Kamali, Jaroslav Opatrny

A Grid Resource Scheduling Algorithm Based on the Utility Optimization

To solve the problem of heterogeneity of user requirements in grid resource allocation, a grid resource scheduling algorithm based on utility function is proposed by analyzing the relationship between the executing time and cost and the user utility function, the theory of economics is used to solve the optimal problem of the user utility function. The result of experiment shows, when the system finished the same set of gridlets, the algorithm achieves better performance not only in cost than the algorithm based on the time optimization when they spent equal time, but also in time than the algorithm based on the cost optimization on the assumption that they consumed the equal quantity of cost.

Jiang Chen, Jian Peng, Xiaoyang Cao

A Generating Method for Internet Topology with Multi-ASes and Multi-tiers

We have understood more precise about the Internet characteristics by the rapid development of the measure, which help us to design new network. In this paper, we analysis the characteristics, including the power-law, hiberarchy and community structure mainly. We propose a new model based on hierarchical framework in accordance with the actual process of internet construct. The model can generate a network topology with multi-ASes and multi-Tiers(MAMT). The result can be applied on new protocol design or network performance evaluate.

Jian-qiang Liu, Jiang-xing Wu, Xiao Huang, Dan Li

A Firm-Growing Model and the Study of Communication Patterns’ Effect on the Structure of Firm’s Social Network

In this article, we propose a firm-growing model, and then collect empirical data to test model validity. The simulation results agree well with the empirical data. We next explore the effect of communication patterns on the growth and structure of firm’s social network and find that the extents to which employees reluctantly interact within or across departments significantly influence the structure of firm’s social network.

Liang Chen, Haigang Li, Zhong Chen, Li Li, Da-Ren He

A Preliminary Study on the Effects of Fear Factors in Disease Propagation

Upon an outbreak of a dangerous infectious disease, people generally tend to reduce their contacts with others in fear of getting infected. Such typical actions apparently help to reduce the outbreak size. Thanks to today’s broad media coverage, the fear factor may also contribute to preventing an outbreak from happening at all. We are motivated to conduct a careful study on modeling and evaluating such effects with a complex network approach. As a first step of this study, we consider the relatively simple case where involved individuals randomly remove a certain fraction of links between them. Analytical and simulation results show that such an action cannot effectively prevent an epidemic outbreak from happening. However, it may significantly reduce the fraction of all the people ever getting infected when an outbreak does happen.

Yubo Wang, Jie Hu, Gaoxi Xiao, Limsoon Wong, Stefan Ma, Tee Hiang Cheng

A Social Network Model Based on Topology Vision

There are many researchers proposed social network models in recent years, and most of them focus on clustering coefficient property of a small-world network and power law degree distribution of a scale-free property. In social network topology, we observed the network is consisted of many nodes with small connectivity and a few high-degree nodes. In the small connectivity part, there are many nodes which have only one degree. Most of past social network models can not generate this part. In this paper, we proposed a social network model based on topology vision and with tunable high hub connectivity. At the same time, we suggested a new characteristic of social network, condensed clustering coefficient, to replace the original clustering coefficient. Finally, this study also includes the analysis of real social network data.

Ping-Nan Hsiao

An Adaptive Strategy for Resource Allocation with Changing Capacities

In this paper, we study a class of resource allocation problems with changing resource capacities. The system consists of competitive agents that have to choose among several resources to complete their tasks. The objective of the resource allocation is that agents can adapt to the dynamic environment autonomously and make good utilisation of resources. We propose an adaptive strategy for agents to use in the resource allocation system with time-varying capacities. This strategy is based on individual agent’s experience and prediction. Simulations show that agents using the adaptive strategy as a whole can adapt effectively to the changing capacity levels and result in better resource utilisation than those proposed in previous work. Finally, we also investigate how the parameters affect the performance of the strategy.

Yingni She, Ho-fung Leung

An Adaptive Markov Chain Monte Carlo Method for GARCH Model

We propose a method to construct a proposal density for the Metropolis-Hastings algorithm in Markov Chain Monte Carlo (MCMC) simulations of the GARCH model. The proposal density is constructed adaptively by using the data sampled by the MCMC method itself. It turns out that autocorrelations between the data generated with our adaptive proposal density are greatly reduced. Thus it is concluded that the adaptive construction method is very efficient and works well for the MCMC simulations of the GARCH model.

Tetsuya Takaishi

Almost Periodicity and Distributional Chaos in Banach Space

Let (

X

, ∥ · ∥) be a Banach space,

f

:

X

X

continous Freche’t differentiable map.Denote the set of almost periodic point by

A

(

f

).In this paper,we prove that there exists an uncountable set

Λ

such that

f

|

Λ

is distributionally chaotic,and

Λ

 ⊂ 

A

(

f

).

Lidong Wang, Shi Tang

Allometric Scaling of Weighted Food Webs

Allometric scaling is an important universal property of metabolic living systems. It also describes the self-similar branching tree-liked structures in transportation networks. This paper presented a new approach to calculate the allometric scaling power law relations for arbitrary flow networks. This method can not only avoid the shortcoming of losing lots of information in the process of generating spanning trees in the conventional approaches but also can be applied to arbitrary weighted networks. The allometric scaling properties of 20 empirical weighted food webs (weights are energy fluxes) are computed according to the new approach, the power law relationships are derived with the universal exponent

η

= 1.0298 which reflects the transportation efficiency of the food webs.

Jiang Zhang

Agent-Based Modeling and Simulation on Emergency Evacuation

Crowd stampedes and evacuation induced by panic caused by emergences often lead to fatalities as people are crushed, injured, trampled or even dead. Such phenomena may be triggered in life-threatening situations such as fires, explosions in crowded buildings. Emergency evacuation simulation has recently attracted the interest of a rapidly increasing number of scientists. This paper presents an Agent-Based Modeling and Simulation using Repast software to construct crowd evacuations for emergency response from an area under a fire. Various types of agents and different attributes of agents are designed in contrast to traditional modeling. The attributes that govern the characteristics of the people are studied and tested by iterative simulations. Simulations are also conducted to demonstrate the effect of various parameters of agents. Some interesting results were observed such as "faster is slower" and the ignorance of available exits. At last, simulation results suggest practical ways of minimizing the harmful consequences of such events and the existence of an optimal escape strategy.

Chuanjun Ren, Chenghui Yang, Shiyao Jin

Adjustable Consensus of Mobile Agent Systems with Heterogeneous Delays

Consensus of mobile agent system is a question with practical significance in the complex dynamics system. In this paper, an adjustable multi-agent moving system with the heterogeneous communication delays is studied under the hypothesis of fixed, undirected and connected topology. The consensus of the multi-agent system is a weighted average consensus that can be adjusted by setting the parameters of the agents. Applying generalized Nyquist criterion, the multi-agent delayed algorithm is analyzed, and many sufficient condition is obtained to ensure the weighted average consensus of the moving system. Finally, many computer simulations are used to show the validity of the results.

Hongyong Yang, Guangdeng Zong

Adaptive Routing Approaches of Controlling Traffic Congestion in Internet

Different routing strategies may result in different behaviors of traffic in internet. We review the routing strategies developed recently in the field of physics and show that the traffic can be significantly improved by the adaptive routing approaches. Comparing with the shortest path approach, the adaptive routing approaches can reduce the over-loading of hub nodes and thus increase the capacity of network. Especially, for the realistic situation with fluctuated traffic, the local self-adjusting traffic awareness protocol can efficiently reduce the traffic congestion. These results provide new insight in sustaining the normal function of Internet.

Zonghua Liu, Ming Tang, Pak Ming Hui

A Study of Tacit Knowledge Transfer Based on Complex Networks Technology in Hierarchical Organizations

In reality, most economic entities are hierarchical organizations. But in the hierarchical organizations tacit knowledge can be transferred across different hierarchies even across different departments. By use of complex networks technology, a hierarchical organization’s framework is modeled in this paper. Through quantifying a number of technical datas we analyze and have a research on the transfer distance and the optimum tacit knowledge transfer path in hierarchy networks.

Tingting Cheng, Hengshan Wang, Lubang Wang

A Stochastic Model for Layered Self-organizing Complex Systems

In this paper we study a problem common to complex systems that dynamically self-organize to an optimal configuration. Assuming the network nodes are of two types, and that one type is subjected to a an upward pressure according to a preferential stochastic model , we wish to determine the distribution of the active nodes over the levels of the network. We generalize the problem to the case of layered graphs as follows. Let

G

be a connected graph with

M

vertices which are divided into

d

levels where the vertices of each edge of

G

belong to consecutive levels. Initially each vertex has a value of 0 or 1 assigned at random. At each step of the stochastic process an edge is chosen at random. Then, the labels of the vertices of this edge are exchanged with probability 1 if the vertex on the higher level has the label 0 and the lower vertex has the label 1. The labels are switched with probability

λ

, if the lower vertex has value of 0 and the higher vertex has the value of 1. This stochastic process has the Markov chain property and is related to random walks on graphs. We derive formulas for the steady state distribution of the number of vertices with label 1 on the levels of the graph.

Yuri Dimitrov, Mario Lauria

A Statistical Study on Oscillatory Protein Expression

Motivated by the experiments on the dynamics of a common network motif,

$\mbox{p53}$

and

$\mbox{Mdm2}$

feedback loop, by Lahav et al. [Nat. Genet

36

, 147(2004)] in individual cells and Lev Bar-or et al. [Proc. Natl. Acad. Sci. USA

97

, 11250(2000)] at the population of cells, we propose a statistical signal-response model with aiming to describe the different oscillatory behaviors for the activities of

$\mbox{p53}$

and

$\mbox{Mdm2}$

proteins both in individual and in population of cells in a unified way. At the cellular level, the activities of

$\mbox{p53}$

and

$\mbox{Mdm2}$

proteins are described by a group of nonlinear dynamical equations where the damage-derived signal is assumed to have the form with abrupt transition (”on”

$\leftrightarrow$

”off”) as soon as signal strength passes forth and back across a threshold. Each cell responses to the damage with different time duration within which the oscillations persist. For the case of population of cells, the activities of

$\mbox{p53}$

and

$\mbox{Mdm2}$

proteins will be the population average of the individual cells, which results damped oscillations, due to the averaging over the cell population with the different response time.

Shiwei Yan

A Comparative Analysis of Specific Spatial Network Topological Models

Creating ensembles of random but “realistic” topologies for complex systems is crucial for many tasks such as benchmark generation and algorithm analysis. In general, explanatory models are preferred to capture topologies of technological and biological complex systems, and some researchers claimed that it is largely impossible to capture any nontrivial network structure while ignoring domain-specific constraints. We study topology models of specific spatial networks, and show that a simple descriptive model, the generalized random graph model (GRG) which only reproduces the degree sequence of complex networks, can closely match the topologies of a variety of real-world spatial networks including electronic circuits, brain and neural networks and transportation networks, and outperform some plausible and explanatory models which consider spatial constraints.

Jun Wang, Gregory Provan

Generalized Greedy Algorithm for Shortest Superstring

In the primitive greedy algorithm for shortest superstring, if a pair of strings with maximum overlap picked out, they are subsequently merged. In this paper, we introduce the concept of optimal set and generalize the primitive greedy algorithm. The generalized algorithm can be reduced to the primitive greedy algorithm if the relative optimal set is empty. Consequently, the new algorithm achieves a better bound at the expense of cost. But the cost is acceptable in practice.

Zhengjun Cao, Lihua Liu, Olivier Markowitch

Extinction and Coexistence in the Internet Market as Complex Networks

A model based on nonautonomous Lotka-Volterra system for web site growth is considered in this paper. Under the conditions that the parameters change with time and the competition conditions are dynamically evaluated, we show that the model exhibits some important characteristics, such as winning alliance and winner-take-all. It is shown that our results are improvement of those of Maurer and Huberman [Journal of Economic Dynamics & Control 27, 2195-2206(2003)], López and Sanjuán [Physica A 301, 512-534(2001)] and López et al. [Physica A 324, 754-758(2003)].

Jiandong Zhao, Liping Fu, Rongfu Cheng, Jiong Ruan

Exponential Synchronization of General Complex Delayed Dynamical Networks via Adaptive Feedback Control

This paper studies the problem of controlling complex delayed dynamical networks by applying adaptive linear feedback controllers to each node. By using Lyapunov functional method, we give the criteria of globally exponential synchronization of the controlled complex delayed dynamical networks. The obtained criteria are suitable to the network with general properties, such as, with hybrid coupling, with different time delays, with asymmetric coupling configuration matrices, and so on. It is useful for future practical engineering design for its universality. Some simulations are given to verify the effectiveness of our scheme.

Haifeng Zhang, Binghong Wang

Exploring and Understanding Scientific Metrics in Citation Networks

This paper explores scientific metrics in citation networks in scientific communities, how they differ in ranking papers and authors, and why. In particular we focus on network effects in scientific metrics and explore their meaning and impact. We initially take as example three main metrics that we believe significant; the standard citation count, the more and more popular h-index, and a variation we propose of PageRank applied to papers (called PaperRank) that is appealing as it mirrors proven and successful algorithms for ranking web pages and captures relevant information present in the whole citation network. As part of analyzing them, we develop generally applicable techniques and metrics for qualitatively and quantitatively analyzing such network-based indexes that evaluate content and people, as well as for understanding the causes of their different behaviors. We put the techniques at work on a dataset of over 260K ACM papers, and discovered that the difference in ranking results is indeed very significant (even when restricting to citation-based indexes), with half of the top-ranked papers differing in a typical 20-element long search result page for papers on a given topic, and with the top researcher being ranked differently over half of the times in an average job posting with 100 applicants.

Mikalai Krapivin, Maurizio Marchese, Fabio Casati

Evolving Specialization, Market and Productivity in an Agent-Based Cooperation Model

This paper introduces an agent-based model in which self-interest intelligent agents are adaptive. Agents can either go to find resources in the environment or mine the resources found. Agents trade information about resources in a market. A biased learning mechanism is introduced to update agents’ capabilities of mining and searching. The learning mechanism plays a vital role in the specialization process in our model. Expectation is also introduced in this paper to determine the trade price. Simulations show that agents can specialize in available capabilities, form market and cooperate to increase their wealth. These emergencies come out through just pre-defining some learning and pricing mechanisms that are not so complex but close to reality. Total productivity and market formation are tracked during the evolving process. The wealth distribution during whole evolving process also demonstrates an interesting power law distribution.

Erbo Zhao, Guo Liu, Dan Luo, Xing’ang Xia, Zhangang Han

Evolving Model of Weighted Networks

In this paper, in order to search the reason of the phenomena of power- law in the weighted networks, we present a general model for the growth of weighted networks that couples of new edges and vertices and the weights’ and intrinsic strengths’ dynamical evolution. This model is based on a simple weight and intrinsic strength driven dynamics and generates networks exhibiting the statistical properties observed in several real-world systems. Within this model we not only yields the scale-free behavior for the weight, strength and degree distributions, but also we give the analytical computation of the distributions of the weight, the strength and the degree .Simultaneity, by way of contrasting our results with those of the random model, we found the preferential attachment is necessary to the phenomena of scale-free of the strength and degree distributions. Finally, we found the analytical results are good consistent with those of numerical simulation. The conclusion from this model is helpful to the investigation of the topological role of weight and strength.

Xianmin Geng, Hongwei Zhou, Guanghui Wen

Evolutionary Prisoner’s Dilemma Game in Flocks

We investigate the effect of mobility on the evolution of cooperation in a flock model, where each player moves on the two-dimensional plane with the same absolute velocity. At each time step every player plays the prisoner’s dilemma game and aligns moving direction with its neighbors, who are chosen according to distances between them in the two-dimensional space. Experimental results have shown that with unconditional cooperation or defection, cooperation can be maintained in mobile players even for high velocities, as local interactions among players are enhanced by the expansion of neighborhood. However, the movement of players can only be offset within a certain range of temptation

b

, while outside this range a rapid decrease of cooperators will appear in the population because too many neighbors are involved.

Zhuo Chen, Jianxi Gao, Yunze Cai, Xiaoming Xu

Evolutionary Game in a Single Hub Structure

In this paper, we investigate the evolutionary game theory on a simplest heterogeneous network-a single hub structure. In order to describe the dynamics on structured populations, we firstly give a general form of a spatial replicator equation. Then according to it, the evolutionary equations describing the evolution of two strategies (cooperation and defection) are derived explicitly and the dynamics of the system is discussed theoretically and numerically. We found if judging the strategy according to its ability to resist the invasion of another, the cooperation does better than the defection. In some parameters when the population

N

is small, an initial D-hub system may evolve to an all-cooperator (AllC) state. All of these phenomena can be well explained by corresponding replicator equation.

Xiaolan Qian, Junzhong Yang

Evolution of the Internet AS-Level Ecosystem

We present an analytically tractable model of Internet evolution at the level of Autonomous Systems (ASs). We call our model the multiclass preferential attachment (MPA) model. As its name suggests, it is based on preferential attachment. All of its parameters are measurable from available Internet topology data. Given the estimated values of these parameters, our analytic results predict a definitive set of statistics characterizing the AS topology structure. These statistics are not part of model formulation. The MPA model thus closes the “measure-model-validate-predict” loop, and provides further evidence that preferential attachment is the main driving force behind Internet evolution.

Srinivas Shakkottai, Marina Fomenkov, Ryan Koga, Dmitri Krioukov, Kc Claffy

European Airlines’ TFP and the 2001 Attack: Towards Safety in a Risk Society

The purpose of this paper is to analyze in terms of security the complexity of European Air Transport after the 2001 terrorist attack, taking into account Total Factor Productivity (T.F.P.) change. Our approach regards European Air Transport as a complex system of airplanes, airports and control. The investigation is based on recent data from the Amadeus database for the largest European (EU-27) air transportation companies (1997-2005). The paper employs the Cobb-Douglas specification of the production function and, in this context, tests the hypothesis that the 2001 terrorist attack had a significant influence on the performance of the EU-27 air transportation companies. An interesting finding is that except for some companies that were negatively influenced, several others were positively influenced by the 2001 terrorist attack. The technological level of the companies included in our dataset remained almost unchanged. The empirical findings are discussed and some suggestions are made regarding policy issues.

Panayotis Michaelides, Kostas Theologou, Angelos Vouldis

Establishing Causality in Complex Human Interactions: Identifying Breakdowns of Intentionality

People in complex scenarios face the challenge of understanding the purpose and effect of other human and computational behaviour on their own goals through intent recognition. They are left asking what caused person or system ‘x’ to do that? The necessity to provide this support human-computer interaction has increased alongside the deployment of autonomous systems that are to some degree unsupervised. This paper aims to examine intent recognition as a form of decision making about causality in complex systems. By finding the needs and limitations of this decision mechanism it is hoped this can be applied to the design of systems to support the awareness of information cues and reduce the number of intent recognition breakdowns between people and autonomous systems. The paper outlines theoretical foundations for this approach using simulation theory and process models of intention. The notion of breakdowns is then applied to intent recognition breakdowns in a diary study to gain insight into the phenomena.

Peter Goodison, Peter Johnson, Joanne Thoms

Extremal Dependencies and Rank Correlations in Power Law Networks

We analyze dependencies in complex networks characterized by power laws (Web sample, Wikipedia sample and a preferential attachment graph) using statistical techniques from the extreme value theory and the theory of multivariate regular variation. To the best of our knowledge, this is the first attempt to apply this well developed methodology to comprehensive graph data. The new insights this yields are striking: the three above-mentioned data sets are shown to have a totally different dependence structure between graph parameters, such as in-degree and PageRank. Based on the proposed approach, we suggest a new measure for rank correlations. Unlike most known methods, this measure is especially sensitive to rank permutations for top-ranked nodes. Using the new correlation measure, we demonstrate that the PageRank ranking is not sensitive to moderate changes in the damping factor.

Yana Volkovich, Nelly Litvak, Bert Zwart

Finding Sales Promotion and Making Decision for New Product Based on Group Analysis of Edge-Enhanced Product Networks

A novel method is proposed in this paper to find the promotive relationship of products from a network point of view. Firstly, a product network is built based on the dataset of handsets’ sale information collected from all outlets of a telecom operator of one province of China, with a period from Jan. 2006 to Jul. 2008. Then the edge enhanced model is applied on product network to divide all the products into several groups, according to which each outlet is assigned to class A or class B for a certain handset. Class A is defined as the outlet which sell the certain handset and contains all of handsets of its group, while other situation for class B which sell the certain handset too. It’s shown from the result of analysis on these two kinds of outlets that many handsets are sold better in outlets of class A than that of class B, even though the sales revenue of all these outlets in the time period is close. That is to say the handsets within a group would promote the sale for each other. Furthermore, a method proposed in this paper gives a way to find out the important attributes of the handsets which lead them to br divided into the same group, and it also explains how to add a new handset to an existing group and where would the new handset be sold best.

Yi Huang, Jianbin Tan, Bin Wu

Fingerprint for Network Topologies

A network’s topology information can be given as an adjacency matrix. The bitmap of sorted adjacency matrix (BOSAM) is a network visualisation tool which can emphasise different network structures by just

looking

at reordered adjacent matrixes. A BOSAM picture resembles the shape of a flower and is characterised by a series of ‘leaves’. Here we show and mathematically prove that for most networks, there is a self-similar relation between the envelope of the BOSAM leaves. This self-similar property allows us to use a single envelope to predict all other envelopes and therefore reconstruct the outline of a network’s BOSAM picture. We analogise the BOSAM envelope to human’s fingerprint as they share a number of common features, e.g. both are simple, easy to obtain, and strongly characteristic encoding essential information for identification.

Yuchun Guo, Changjia Chen, Shi Zhou

Generalized Farey Tree Network with Small-World

Generalized Farey tree network (GFTN) model with small-world is proposed, and the topological characteristics are studied by both theoretical analysis and numerical simulations, which are in good accordance with each other. Analytical results show that the degree distribution of the GFTN is exponential. As the number of network nodes increasing with time interval (or level number),

t

, the clustering coefficient of the networks tends to a constant, ln2; the diameter of the network is increasing with

t

, the resulting networks are evolved from disassortative to assortative and show assortative coefficient tends to 0.25 for large

t

.

Jin-Qing Fang, Yong Li

Fuzzy Entropy Method for Quantifying Supply Chain Networks Complexity

Supply chain is a special kind of complex network. Its complexity and uncertainty makes it very difficult to control and manage. Supply chains are faced with a rising complexity of products, structures, and processes. Because of the strong link between a supply chain’s complexity and its efficiency the supply chain complexity management becomes a major challenge of today’s business management. The aim of this paper is to quantify the complexity and organization level of an industrial network working towards the development of a ‘Supply Chain Network Analysis’ (SCNA). By measuring flows of goods and interaction costs between different sectors of activity within the supply chain borders, a network of flows is built and successively investigated by network analysis. The result of this study shows that our approach can provide an interesting conceptual perspective in which the modern supply network can be framed, and that network analysis can handle these issues in practice.

Jihui Zhang, Junqin Xu

Further Study on Proxy Authorization and Its Scheme

Proxy authorization makes it possible to entrust the right of signing or making decisions to other parties. This paper analyzes the basic principles and security problems of proxy authorization schemes and presents three proxy authorization schemes based on elliptic curves cryptosystem. In the first multi-party proxy authorization scheme, a group of n members can cooperate to entrust their right, and the authorizing right can be supervised by secret sharing mechanism. In the second multicast proxy authorization scheme, the members can entrust their right in multicast mode. The multicasting design strategy prevents coalition attack, avoids the problem of generalized signature forgery. In the last conditionally anonymous scheme, the identity blinding algorithm enables the proxy signer to be anonymous and the anonymity can also be revoked if necessary. This design strategy avoids the misuse of proxy authorization and renders effective supervision on signature entrusting and proxy signing.

Xuanwu Zhou, Yang Su, Ping Wei

Funnelling Effect in Networks

Funnelling effect, in the context of searching on networks, precisely indicates that the search takes place through a few specific nodes. We define the funnelling capacity

f

of a node as the fraction of successful dynamic paths through it with a fixed target. The distribution

D

(

f

) of the fraction of nodes with funnelling capacity

f

shows a power law behaviour in random networks (with power law or stretched exponential degree distribution) for a considerable range of values of the parameters defining the networks. Specifically we study in detail

D

1

 = 

D

(

f

 = 1), which is the quantity signifying the presence of nodes through which all the dynamical paths pass through. In scale free networks with degree distribution

P

(

k

) ∝ 

k

 − 

γ

,

D

1

increases linearly with

γ

initially and then attains a constant value. It shows a power law behaviour,

$D_1 \propto N^{-\rho}$

, with the number of nodes

N

where

ρ

is weakly dependent on

γ

for

γ

> 2.2. The latter variation is also independent of the number of searches. On stretched exponential networks with

$P(k) \propto \exp{(-k^\delta)}$

,

ρ

is strongly dependent on

δ

. The funnelling distribution for a model social network, where the question of funnelling is most relevant, is also investigated.

Parongama Sen

Frequency Domain Analysis of a Stochastic Biological Network Motif with Delay

In this paper, a set of delay stochastic differential equations, which involves the mechanisms of intrinsic and extrinsic noises, time delays and negative feedback, is proposed to describe the nonlinear dynamics within a general biological network motif. Frequency domain analysis method is exploited to study the interplays among such mechanisms.

Qi Wang, Shiwei Yan, Shengjun Liu, Xian Li

Frequency Distributions of Sand Pile Models

We calculated the frequency distributions of cluster sizes in the sand pile models. Two cellular automata models differing in the rules of adding sand particles are used. For the model with local perturbation only, the distribution shows a power law behavior regardless of the spatial dimension that the sand pile is situated at. For the other model where the perturbation generated by the addition of a sand particle is not confined to one site only, the distribution is generally a power law plus an exponential cutoff. These results are consistent with what was found previously for another complex system using a model of constrained minority game. The frequency distributions in higher dimensions than two are also calculated and discussed.

Ruey-Tarng Liu

Framework for Visualisation of Cancer Tumours

This paper discusses the use of Fister-Panetta model in the visualisation of cancerous growths. Cancer evolution and the associated proper medication strategy is an example of such a complex problem that requires an interdisciplinary approach in order to be properly addressed. The paper addresses some basic aspects regarding how cancer research could benefit from the cooperation between mathematics and biology, describes how to model and visualize cancer tumor with recursive algorithms and Fister and Panetta pattern.

Yin Jie Chen, Razvan Bocu, Mark Tangney, Sabin Tabirca

FLECS: A Framework for Rapidly Implementing Forwarding Protocols

Design, implementation and deployment of network protocols is a challenging and difficult task. Determining their correctness and feasibility for large-scale networks is even more complicated. This paper presents

Flecs

, a framework for fascilitating implementation of forwarding protocols for packet-switched networks. We build upon the observation that the forwarding functionality can be modeled as a combination of well-defined but customizable components, the functionality of each component is constrained by the fundamental axioms of communication.

Flecs

provides a protocol specification language and automatically generates the protocol implementation from the specification.

Mirza Beg

Firm Size Distribution in Fortune Global 500

By analyzing the data of Fortune Global 500 firms from 1996 to 2008, we found that their ranks and revenues always obey the same distribution, which implies that worldwide firm structure has been stable for a long time. The fitting results show that simple Zipf distribution is not an ideal model for global firms, while SCL, FSS have better fitting goodness, and lognormal fitting is the best. And then, we proposed a simple explanation.

Qinghua Chen, Liujun Chen, Kai Liu

Finite Time Ruin Probability in Non-standard Risk Model with Risky Investments

In this paper, under the assumption that the claimsize is subexponentially distributed and the insurance capital is totally invested in risky asset, some simple asymptotics of finite horizon ruin probabilities are obtained for non-homogeneous Poisson process and conditional Poisson risk models as well as renewal risk model, when the initial capital is quite large. Extremal event is described in this case because some claim can be larger than initial capital even it is large enough. The results obtained extended the corresponding results of related papers in this area.

Tao Jiang

Epidemic Self-synchronization in Complex Networks

In this article we present and evaluate an epidemic algorithm for the synchronization of coupled Kuramoto oscillators in complex network topologies. The algorithm addresses the problem of providing a global, synchronous notion of time in complex, dynamic Peer-to-Peer topologies. For this it requires a periodic coupling of nodes to a single random one-hop-neighbor. The strength of the nodes’ couplings is given as a function of the degrees of both coupling partners. We study the emergence of self-synchronization and the resilience against node failures for different coupling strength functions and network topologies. For Watts/Strogatz networks, we observe critical behavior suggesting that small-world properties of the underlying topology are crucial for self-synchronization to occur. From simulations on networks under the effect of churn, we draw the conclusion that special coupling functions can be used to enhance synchronization resilience in power-law Peer-to-Peer topologies.

Ingo Scholtes, Jean Botev, Markus Esch, Peter Sturm

Entropy Based Detection of DDoS Attacks in Packet Switching Network Models

Distributed denial-of-service (DDoS) attacks are network-wide attacks that cannot be detected or stopped easily. They affect “natural” spatio-temporal packet traffic patterns, i.e. “natural distributions” of packets passing through the routers. Thus, they affect “natural” information entropy profiles, a sort of “fingerprints”, of normal packet traffic. We study if by monitoring information entropy of packet traffic through selected routers one may detect DDoS attacks or anomalous packet traffic in packet switching network (PSN) models. Our simulations show that the considered DDoS attacks of “ping” type cause shifts in information entropy profiles of packet traffic monitored even at small sets of routers and that it is easier to detect these shifts if static routing is used instead of dynamic routing. Thus, network-wide monitoring of information entropy of packet traffic at properly selected routers may provide means for detecting DDoS attacks and other anomalous packet traffics.

Anna T. Lawniczak, Hao Wu, Bruno Di Stefano

Enhancing the Scale-Free Network’s Attack Tolerance

Despite the large size of most communication systems such as the Internet and World Wide Web (WWW), there is a relatively short path between two nodes, revealing the networks’ small world characteristic which speeds the delivery of information and data. While these networks have a surprising error tolerance, their scale-free topology makes them fragile under intentional attack, leaving us a challenge on how to improve the networks’ robustness against attack without losing their small world merit. Here we try to enhance scale-free network’s tolerance under attack by using a method based on networks’ topology re-constructing.

Zehui Qu, Pu Wang, Zhiguang Qin

Degree-Distribution Stability of Growing Networks

In this paper, we abstract a kind of stochastic processes from evolving processes of growing networks, this process is called growing network Markov chains. Thus the existence and the formulas of degree distribution are transformed to the corresponding problems of growing network Markov chains. First we investigate the growing network Markov chains, and obtain the condition in which the steady degree distribution exists and get its exact formulas. Then we apply it to various growing networks. With this method, we get a rigorous, exact and unified solution of the steady degree distribution for growing networks.

Zhenting Hou, Xiangxing Kong, Dinghua Shi, Guanrong Chen, Qinggui Zhao

Degree Distribution of a Two-Component Growing Network

We propose a two-component growing network model which comprises two kinds of nodes. Such a network is constructed by introducing new nodes of either kind with no immediate links and creating new links between any two nodes. We then investigate the connectivity of the two-component growing network by means of the rate equation approach. For a network system with shifted linear connection rate kernels, the in-degree and out-degree distributions take power-law forms; while for a random growing network, the in-degree and out-degree distributions are both exponential. Moreover, the in-degree and out-degree distributions are correlated each other.

Jianhong Ke, Xiaoshuang Chen

Correlation Properties and Self-similarity of Renormalization Email Networks

A degree-thresholding renormalization method is recently introduced to find topological characteristics of some complex networks. As a matter of fact, the applicability of these characteristics depends on the level or the type of complex networks. Here, a modified version of this original algorithm is presented to unravel ubiquitous characteristics of observed email networks and obtain correct understanding of underlying evolutionary mechanism. Some topology metrics of the email networks under renormalization were analyzed. The results show that renormalization email networks have the power-law distribution with double exponents, are disassortative and become assortative after half of total renormalization steps, have high-clustering coefficients and rich-club phenomena. These characteristics are self-similar both before and after renormalization until half of total renormalization steps, otherwise are self-dissimilar.

Lianming Zhang, Sundong Liu, Yuling Tang, Hualan Xu

Constructing Searchable P2P Network with Randomly Selected Long-Distance Connections

Object lookup is a basic problem in P2P network. Long-distance connections have been used to construct a searchable P2P network according to the small-world paradigm. Long-distance connections based on distance can achieve a searchable P2P network in theorem. However, it is hard to measure the lattice distance between two peers in real P2P network. On the other hand, it is easy to construct randomly selected long-distance connections with low overhead. We increase the number of randomly selected long-distance connections

k

to improve the performance of object lookup. Simulation results show there is some relation among

k

, the network size

N

and the average path length. The lower bound of

k

to achieve a searchable P2P network is still an open question.

Jingbo Shen, Jinlong Li, Xufa Wang

Conservation of Edge Essentiality Profiles in Metabolic Networks Across Species

Reactions involved in cellular metabolism form a complex network susceptible to targeted attacks. Recent experiments show that several descriptors of edge essentiality correlate well with lethality of silencing corresponding genes in a model organism, opening path to identifying targets for antimicrobial drugs that would disrupt network functioning in bacteria. However, correlation of high essentiality with experiment is necessary but not sufficient for a descriptor to be useful. Also, the essentialities of corresponding edges have to differ markedly between pathogens and hosts, to yield minimal effect on the latter. Here, we analyse similarity of profiles of several edge essentiality measures across multiple species. We show that local measures, based on degrees of a substrate and a product linked by the edge, or on the alternative paths connecting the two, are evolutionarily conserved within bacteria, archaea and eukaryotes, but also differ between these groups, leading to isolated clusters of species. Furthermore, comparison with a global topological measure, the relative decrease in network efficiency upon edge removal, shows that metabolic networks are more conserved locally than globally.

Tomasz Arodź

Consensus Seeking and Controlling over Directed Delayed Networks

In this paper, we are concerned about the consensus problem for multi-agent systems in the presence of directed information flow and arbitrary communication delays. For each agent, only delayed local information can be used to adjust its value. Information flow between connected agents can be asymmetric. It will be shown that, whatever the communication delays are in principle, consensus will eventually be achieved for strongly connected networks. Furthermore, a local controller is designed for one of the agents (but not for each agent) to control all the agents. Very weak feedback strength is proved to be effective for the control of multi-agent systems. Numerical simulations are also performed to verify our theoretical analysis.

Jianquan Lu, Daniel W. C. Ho

Complex Systems in Cosmology: “The Antennae” Case Study

Due to its particular shape, “The Antennae” is a well-known complex cosmological dynamical structure. Classical simulations of this phenomenon are based on “top-down” models that required thousands of point-mass particles. We describe an approach for cosmological simulation, based on a hierarchical multi-agent system, and evidence is shown that this approach significantly reduces the number of elements needed to simulate “The Antennae” structure.

Jean-Claude Torrel, Claude Lattaud, Jean-Claude Heudin

Complex Modelling of Open System Design for Sustainable Architecture

This paper argues a model of complex system design for sustainable architecture within a framework of entropy evolution. The spectrum of sustainable architecture consists of the efficient use of energy and material resource in life-cycle of buildings, the active involvement of the occupants in micro-climate control within buildings, and the natural environmental context. The interactions of the parameters compose a complex system of sustainable architectural design, of which the conventional linear and fragmented design technologies are insufficient to indicate holistic and ongoing environmental performance. The complexity theory of dissipative structure states a microscopic formulation of open system evolution, which provides a system design framework for the evolution of building environmental performance towards an optimization of sustainability in architecture.

Yan Gu, John Frazer

Comparing Networks from a Data Analysis Perspective

To probe network characteristics, two predominant ways of network comparison are global property statistics and subgraph enumeration. However, they suffer from limited information and exhaustible computing. Here, we present an approach to compare networks from the perspective of data analysis. Initially, the approach projects each node of original network as a high-dimensional data point, and the network is seen as clouds of data points. Then the dispersion information of the principal component analysis (PCA) projection of the generated data clouds can be used to distinguish networks. We applied this node projection method to the yeast protein-protein interaction networks and the Internet Autonomous System networks, two types of networks with several similar higher properties. The method can efficiently distinguish one from the other. The identical result of different datasets from independent sources also indicated that the method is a robust and universal framework.

Wei Li, Jing-Yu Yang

Community Structure Detection in Complex Networks with Applications to Gas-Liquid Two-Phase Flow

We propose an algorithm to detect community structure in complex networks based on data field theory. The efficiency and accuracy of the algorithm for computer-simulated and real networks make it feasible to be used for the accurate detection of community structure in complex networks. Using the conductance fluctuating signals measured from gas-liquid two-phase flow dynamic experiments, we construct the flow pattern complex network. With the applications of the community-detection algorithm to the flow pattern complex network, we achieve good identification of flow pattern in gas-liquid two-phase flow. In this paper, from a new perspective, we not only present a new community-detection algorithm based on data field theory, but also build a bridge between complex network and two-phase flow.

Zhongke Gao, Ningde Jin

Design of Multilphase Sinusoidal Oscillator Based on FTFN

A new multiphase sinusoidal oscillator is presented. The circuit realization uses the four-terminal floating nullor (FTFN) to generate arbitrary n current sinusoidal signals equally spaced in phase. The proposed circuit consists of n CCCIIs, n grounded capacitors and 2n grounded resistors. The oscillation condition and oscillation frequency are independently controlled. The former depends on the grounded resistance

R

1

and the latter depends on the grounded capacitor

C

. The circuit also enjoys having simple structure and very low component count and it is highly suitable for monolithic implementation.

YanHui Xi, LiangYu Peng

Designing Capital-Intensive Systems with Architectural and Operational Flexibility Using a Screening Model

Development of capital intensive systems, such as offshore oil platforms or other industrial infrastructure, generally requires a significant amount of capital investment under various resource, technical, and market uncertainties. It is a very challenging task for development co-owners or joint ventures because important decisions, such as system architectures, have to be made while uncertainty remains high. This paper develops a screening model and a simulation framework to quickly explore the design space for complex engineering systems under uncertainty allowing promising strategies or architectures to be identified. Flexibility in systems’ design and operation is proposed as a proactive means to enable systems to adapt to future uncertainty. Architectural and operational flexibility can improve systems’ lifecycle value by mitigating downside risks and capturing upside opportunities. In order to effectively explore different flexible strategies addressing a view of uncertainty which changes with time, a computational framework based on Monte Carlo simulation is proposed in this paper. This framework is applied to study flexible development strategies for a representative offshore petroleum project. The complexity of this problem comes from multi-domain uncertainties, large architectural design space, and structure of flexibility decision rules. The results demonstrate that architectural and operational flexibility can significantly improve projects’ Expected Net Present Value (ENPV), reduce downside risks, and improve upside gains, compared to adopting an inflexible strategy appropriate to the view of uncertainty at the start of the project. In this particular case study, the most flexible strategy improves ENPV by 85% over an inflexible base case.

Jijun Lin, Olivier de Weck, Richard de Neufville, Bob Robinson, David MacGowan

Detecting Gross Errors for Steady State Systems

Gross error detection is important to data reconciliation in process industry. In practice, gross errors cannot be identified exactly by any algorithm. The issue of unreasonable solutions of gross error detection algorithms is discussed. A novel mixed integer optimization method presented in a previous paper is used in this paper. A strategy is proposed to identify gross errors and its most possible alternatives for steady state systems by the method. Gross errors are identified without the need for measurements elimination. The computation results show the effectiveness of the proposed strategy.

Congli Mei

Enhancing Synchronization in Systems of Non-identical Kuramoto Oscillators

In this paper we present a summary of some of our recent results on the synchronization of non-identical Kuramoto oscillators coupled via complex networks. Crucially, we emphasize that the systems overall degree of synchronization cannot only be improved by tuning properties of the coupling network, but also by a correlated assignment of oscillators to nodes. In the context of symmetrical coupling via undirected networks we discuss network characteristics and correlations between the oscillator placement on the nodes of the network that enhance the overall degree of synchronization. Several simple rules to improve the degree of synchronization in a system are given, such as, e.g. (i) anti-correlated placement of adjacent oscillators, and (ii) placement of oscillators with native frequencies far off the mean in the centre of the network and (iii) placement of oscillators with native frequencies close to the mean at the periphery of the network. The influence of oscillator correlations on synchronization transition is discussed as well. Finally, we analyze the question whether a a globally synchronized system can be generated by rewirings that improve the local synchronizability.

Markus Brede

Enhancement of Synchronizability of the Kuramoto Model with Assortative Degree-Frequency Mixing

Assortative mixing feature is an important topological property in complex networks. In this paper, we extend degree-degree mixing feature to non-identical nodes networks. We propose the degree-frequency correlation coefficient to measure the correlations between the degree and the natural frequency of oscillators. We find that the perfect assortative degree-frequency network is quite easy to synchronize. We also investigate the synchronization of complex networks with different degree-frequency coefficient.

Jin Fan, David J. Hill

Emergence of Scale-Free Networks with Seceding Mechanism

In order to explore further the underlying mechanism of the scale-free networks, we study stochastic secession as a mechanism for the creation of complex networks. In this evolution the network growth incorporates the addition of new links between existing nodes, the deleting and rewiring of some existing links, and the stochastic secession of nodes. To random growing networks with preferential attachment, the model yields scale-free behavior for the degree distribution. Furthermore, we get the analytical expression of the power law degree distribution with scaling exponent

γ

ranges from 1.1 to 9. The analytical expressions are in good agreement with the numerical simulation results.

Xian-Min Geng, Guang-Hui Wen, Shu-Chen Wan, Jie-Yu Xiong

Emergence and Simulation

One approach to characterizing the elusive notion of emergence is to define that a property is emergent if and only if its presence can be derived but only by simulation. In this paper I investigate the pros and cons of this approach, focusing in particular on whether an appropriately distinct boundary can be drawn between simulation-based and non-simulation-based methods. I also examine the implications of this definition for the epistemological role of emergent properties in prediction and in explanation.

Alan Baker

Ecological Research of the Voluntary Disclosure about Listed Companies

In the paper, the research subject is the ecological relationship between the Small and Medium-sized Enterprises(SMEs) and the Large-scale Enterprises(Les). From the perspective of ecology, setting up the competitive model basic on the Logistic model, and carrying out further analysis about the voluntary information disclosure of listed company, then getting the strategic choice about the voluntary information disclosure and the ecological explanation of false information, and the dynamic mechanism and strategy of the voluntary information disclosure of listed company.

Jing-Jing Hu, Guang-Le Yan

Dynamics of Research Team Formation in Complex Networks

Most organizations encourage the formation of teams to accomplish complicated tasks, and vice verse, effective teams could bring lots benefits and profits for organizations. Network structure plays an important role in forming teams. In this paper, we specifically study the dynamics of team formation in large research communities in which knowledge of individuals plays an important role on team performance and individual utility. An agent-based model is proposed, in which heterogeneous agents from research communities are described and empirically tested. Each agent has a knowledge endowment and a preference for both income and leisure. Agents provide a variable input (‘effort’) and their knowledge endowments to production. They could learn from others in their team and those who are not in their team but have private connections in community to adjust their own knowledge endowment. They are allowed to join other teams or work alone when it is welfare maximizing to do so. Various simulation experiments are conducted to examine the impacts of network topology, knowledge diffusion among community network, and team output sharing mechanisms on the dynamics of team formation.

Caihong Sun, Yuzi Wan, Yu Chen

Dynamic Regimes of a Multi-agent Stock Market Model

This paper presents a stochastic multi-agent model of stock market. The market dynamics include switches between chartists and fundamentalists and switches in the prevailing opinions (optimistic or pessimistic) among chartists. A nonlinear dynamical system is derived to depict the underlying mechanisms of market evolvement. Under different settings of parameters representing traders’ mimetic contagion propensity, price chasing propensity and strategy switching propensity, the system exhibits four kinds of dynamic regimes: fundamental equilibrium, non-fundamental equilibrium, periodicity and chaos.

Tongkui Yu, Honggang Li

Differential Forms: A New Tool in Economics

Econophysics is the transfer of methods from natural to socio-economic sciences. This concept has first been applied to finance

1

, but it is now also used in various applications of economics and social sciences [2,3]. The present paper focuses on problems in macro economics and growth. 1. Neoclassical theory [4, 5] neglects the “ex post” property of income and growth. Income Y(K, L) is assumed to be a function of capital and labor. But functions cannot model the “ex post” character of income. 2. Neoclassical theory is based on a Cobb Douglas function [6] with variable elasticity

α

, which may be fitted to economic data. But an undefined elasticity

α

leads to a descriptive rather than a predictive economic theory. The present paper introduces a new tool - differential forms and path dependent integrals - to macro economics. This is a solution to the problems above: 1. The integral of not exact differential forms is path dependent and can only be calculated “ex post” like income and economic growth. 2. Not exact differential forms can be made exact by an integrating factor, this leads to a new, well defined, unique production function F and a predictive economic theory.

Jürgen Mimkes

Development of Road Traffic CA Model of 4-Way Intersection to Study Travel Time

We describe our development of a road traffic CA (Cellular Automata) model of the four most common types of 4-way intersection (Yield-controlled intersections, Stop-controlled intersections, Signal-controlled intersections, and Roundabout-based intersection). We developed this model to study how these four different types of 4-way intersection affect road traffic flow and congestion in general and “travel time” in particular. In this paper we describe the model and 4WayCA.exe, the traffic simulator software package in which the model has been implemented. We focus in particular on the model abstractions and on the simulator architecture.

Anna T. Lawniczak, Bruno N. Di Stefano

Community Identification in Directed Networks

The most common approach to community identification of directed networks has been to ignore edge directions and apply methods developed for undirected networks. Recently, Leicht and Newman published a work on community identification of directed networks, which is a generalization of the widely used community finding technique of modularity maximization in undirected networks. However, our investigation of this method shows that the method they used does not exploit direction information as they proposed. In this work, we propose an alternative method which exploits the directional information of links properly.

Youngdo Kim, Seung-Woo Son, Hawoong Jeong

Complex Multi-modal Multi-level Influence Networks - Affordable Housing Case Study -

Most influence networks are depicted as nodes and links operating in the manner of a feed-forward neural network where both nodes and links appear to be homogenous in their nature. Experience has shown that not only do these networks fail to deal adequately with reality, but also that practitioners struggle to understand why. This paper addresses this challenge by examining the rich, multi-level and multi-modal nature of influence networks and proposes an approach drawing inspiration from complexity science - leading to multi-perspective techniques which enable influence networks to be used to more effectively to capture, visualise and understand complex situations, so providing insights to support effective decision-making. The paper gives evidence (from a case study looking at the provision of affordable housing in the UK) which illustrates how the techniques have been employed and what benefits accrued.

Patrick Beautement, Christine Brönner

Collective Aggregation Pattern Dynamics Control via Attractive/Repulsive Function

In the coordinated collective behaviors of biological swarms and flocks, the attractive/repulsive (A/R) functional link between each pair of particles plays an important role. By changing the slope of the A/R function, a dramatic transition between different aggregation patterns surfaces. With a high value of the slope, the particle aggregation shows a liquid-like pattern in which the outer particles are sparsely distributed while the inner ones densely. In addition, the particle density is reduced from the outside to the inside of each cluster. By comparison, when the slope decreases to a sufficiently low value, the particle aggregation exhibits a crystal-like pattern as the distance between each pair of neighboring particles remains constant. Remarkably, there is an obvious spinodal in the curve of particle-particle distance variance versus the slope, indicating a transition between liquid-like and crystal-like aggregation patterns. Significantly, this work may reveal some common mechanism behind the aggregation of physical particles and swarming of organisms in nature, and may find its potential engineering applications, for example, UAVs and multi-robot systems.

Michael Z. Q. Chen, Zhao Cheng, Hai-Tao Zhang, Tao Zhou, Ian Postlethwaite

Transforming Time Series into Complex Networks

We introduce transformations from time series data to the domain of complex networks which allow us to characterise the dynamics underlying the time series in terms of topological features of the complex network. We show that specific types of dynamics can be characterised by a specific prevalence in the complex network motifs. For example, low-dimensional chaotic flows with one positive Lyapunov exponent form a single family while noisy non-chaotic dynamics and hyper-chaos are both distinct. We find that the same phenomena is also true for discrete map-like data. These algorithms provide a new way of studying chaotic time series and equip us with a wide range of statistical measures previously not available in the field of nonlinear time series analysis.

Michael Small, Jie Zhang, Xiaoke Xu

Power Law Modelling of Internet Topology

In recent years there have been tremendous efforts to measure, characterise and model the internet topology. We discuss why the power law degree distribution is not an artifact but an integral property of the internet. On the other hand we argue that while it is one of the properties that fundamentally characterise the global internet structure, other properties should also be considered to obtain a full description of the network. We review the power law modelling of the internet topology and provide a critical look at the contribution of such research to the Internet engineering.

Shi Zhou

Observing Stock Market Fluctuation in Networks of Stocks

In this paper we study the structural variation of the network formed by connecting Standard & Poor’s 500 (S&P500) stocks whose closing prices (or price returns) are highly correlated. Specifically we consider S&P500 stocks that were traded from January 1, 2000 to December 31, 2004, and construct complex networks based on cross correlation between the time series of the closing prices (or price returns) over a fixed period of time. A simple threshold approach is used for establishing connections between stocks. The period over which the network is constructed is 20 trading days, which should be long enough to produce meaningful cross correlation values, but sufficiently short in order to avoid averaging effects that smooth off the salient fluctuations. A network is constructed for each 20-trading-day window in the entire trading period under study. The window moves at a 1-trading-day step. The power-law exponent is determined for each window, along with the corresponding mean error of the power law approximation which reflects how closely the degree distribution resembles a scalefree-like distribution. The key finding is that the scalefreeness of the degree distribution is disrupted when the market experiences fluctuation. Thus, the mean error of the power-law approximation becomes an effective indicative parameter of the volatility of the stock market.

C. K. Tse, J. Liu, F. C. M. Lau, K. He

Networks That Optimize a Trade-Off between Efficiency and Dynamical Resilience

In this paper we study networks that have been optimized to realize a trade-off between communication efficiency and dynamical resilience. While the first is related to the average shortest pathlength, we argue that the second can be measured by the largest eigenvalue of the adjacency matrix of the network. Best efficiency is realized in star-like configurations, while enhanced resilience is related to the avoidance of short loops and degree homogeneity. Thus crucially, very efficient networks are not resilient while very resilient networks lack in efficiency. Networks that realize a trade-off between both limiting cases exhibit core-periphery structures, where the average degree of core nodes decreases but core size increases as the weight is gradually shifted from a strong requirement for efficiency and limited resilience towards a smaller requirement for efficiency and a strong demand for resilience.

We argue that both, efficiency and resilience are important requirements for network design and highlight how networks can be constructed that allow for both.

Markus Brede, Bert J. M. de Vries

Modelling of Epidemics with a Generalized Nonlinear Incidence on Complex Networks

In this paper the spreading of epidemic model on complex networks with a generalized nonlinear incidence rate is presented. Firstly the SIS model on homogeneous networks with nonlinear incidence rate is considered, and the existence conditions about the disease-free equilibrium and the endemic equilibrium are given. And then the model on heterogenous scale-free (SF) networks is considered, where the absence of the threshold on SF networks with nonlinear incidence is demonstrated. At last the stability of the disease-free equilibrium on SF networks is obtained. From this paper, it is shown that, while the number of the equilibria is indeed different from the corresponding model with linear incidence rate, the basic reproductive number, which determinate whether the disease is spreading or not, is independent of the functional form of the nonlinear incidence rate.

Maoxing Liu, Jiong Ruan

Modeling Failure Propagation in Large-Scale Engineering Networks

The simultaneous unavailability of several technical components within large-scale engineering systems can lead to high stress, rendering them prone to cascading events. In order to gain qualitative insights into the failure propagation mechanisms resulting from independent outages, we adopt a minimalistic model representing the components and their interdependencies by an undirected, unweighted network. The failure dynamics are modeled by an anticipated accelerated “wearout” process being dependent on the initial degree of a node and on the number of failed nearest neighbors. The results of the stochastic simulations imply that the influence of the network topology on the speed of the cascade highly depends on how the number of failed nearest neighbors shortens the life expectancy of a node. As a formal description of the decaying networks we propose a continuous-time mean field approximation, estimating the average failure rate of the nearest neighbors of a node based on the degree-degree distribution.

Markus Schläpfer, Jonathan L. Shapiro

Modeling and Dynamical Analysis of Molecular Networks

One of major challenges for post-genomic biology is to understand how molecules dynamically interact to form networks which facilitate sophisticated biological functions. Instead of analyzing individual molecules, systems biology is to study dynamical networks of interacting molecules which give rise to life. In recent years, many progress have been made in systematic approaches and high-throughput technologies for systematic studying complex molecular networks. Analyzing these networks provides novel insights in understanding not only complicated cellular phenomena but also the essential principles or fundamental mechanisms behind the phenomena at system level. This paper presents a brief survey on recent developments on modeling and analyzing complex molecular networks mainly from global and dynamical properties of complex molecular networks. Some recent developments and perspectives of analysis on molecular networks are also discussed.

Ruiqi Wang, Xing-Ming Zhao, Zengrong Liu

Eigenvalue Based Stability Analysis for Asymmetric Complex Dynamical Networks

The problem of stabilization in complex networks with asymmetric couplings forced by pinning control is studied. By using eigenvalue analysis, controllable regions for different types of coupling links are obtained. Some relevant factors on controllability such as pinning fraction and pinning strength are also investigated.

Zengqiang Chen, Linying Xiang, Zhongxin Liu, Zhuzhi Yuan, Kai Chang

Collective Behavior Coordination and Aggregation with Low-Cost Communication

An important natural phenomenon surfaces that satisfactory synchronization of self-driven particles can be achieved via remarkably reduced communication cost, especially for high density particle groups with low external noise. Statistical numerical evidence illustrates that a highly efficient manner is to distribute the communication messages as evenly as possible along the whole dynamic process, since it minimizes the communication redundancy. More surprisingly, it is discovered that there exists an abnormal region in the state diagram where moderately decreasing the communication cost can even improve the synchronization performance. Significantly, another interesting fact is found that low-cost communication can help the particles aggregate into synchronized clusters, which may be beneficial to explain the forming mechanism of individuals’ aggregation phenomena over biological flocks/swarms.

Hai-Tao Zhang, Michael Z. Q. Chen, Tao Zhou, Zhao Cheng, Pin-Ze Yu

Visual Analysis of Complex Networks and Community Structure

Many real-world domains can be represented as complex networks.A good visualization of a large and complex network is worth more than millions of words. Visual depictions of networks, which exploit human visual processing, are more prone to cognition of the structure of such complex networks than the computational representation. We star by briefly introducing some key technologies of network visualization, such as graph drawing algorithm and community discovery methods. The typical tools for network visualization are also reviewed. A newly developed software framework JSNVA for network visual analysis is introduced. Finally,the applications of JSNVA in bibliometric analysis and mobile call graph analysis are presented.

Bin Wu, Qi Ye, Yi Wang, Ran Bi, Lijun Suo, Deyong Hu, Shengqi Yang

Complex Phenomena in Orchestras – Metaphors for Leadership and Enterprise

This paper recognises that comparisons have been made between the role of the conductor of an orchestra and leaders of enterprises, but that little note has been taken of how the complex dynamics of orchestras can provide metaphors for transformational and / or evolutionary behaviour in complex enterprises. The paper intends to identify some of the dynamic musical patterns and phenomena that exist in orchestras and show how these can provide insights for other domains where similar complex federated structures emerge ’on-the-fly’ by providing and using a complexity-inspired framework.

Patrick Beautement, Christine Brönner

Composing Music with Complex Networks

In this paper we study the network structure in music and attempt to compose music artificially. Networks are constructed with nodes and edges corresponding to musical notes and their co-occurrences. We analyze sample compositions from Bach, Mozart, Chopin, as well as other types of music including Chinese pop music. We observe remarkably similar properties in all networks constructed from the selected compositions. Power-law exponents of degree distributions, mean degrees, clustering coefficients, mean geodesic distances, etc. are reported. With the network constructed, music can be created by using a biased random walk algorithm, which begins with a randomly chosen note and selects the subsequent notes according to a simple set of rules that compares the weights of the edges, weights of the nodes, and/or the degrees of nodes. The newly created music from complex networks will be played in the presentation.

Xiaofan Liu, Chi K. Tse, Michael Small

Hopfield’s Model of Patterns Recognition and Laws of Artistic Perception

The model of patterns recognition or attractor network model of associative memory, offered by J.Hopfield 1982, is the most known model in theoretical neuroscience. This paper aims to show, that such well-known laws of art perception as the Wundt curve, perception of visual ambiguity in art, and also the model perception of musical tonalities are nothing else than special cases of the Hopfield’s model of patterns recognition.

Igor Yevin, Alexander Koblyakov

Music, New Aesthetic and Complexity

This paper illustrates an algorithm to generate a complex acoustic stimulus whose statistical properties are as close as possible to the non-stationary dynamics revealed by the current analysis of the electro-encephalogram activity of the human brain. Thus, the composition is driven by crucial events, namely renewal non-Poisson events with an inter-time distribution density

ψ

(

τ

), which is an inverse power law with index

μ

, fitting the condition 1 ≤ 

μ

 ≤ 2. We find that the music composition is more attractive when we fill the time region between two consecutive crucial events so as to enhance the leading role of

μ

. In all cases the spectra markedly depart from the ideal 1/

f

condition, thereby suggesting a shift from the 1/

f

noise perspective of the pioneer work of Voss and Clark to the Zipf’s law perspective advocated by more recent work on music composition.

David Adams, Paolo Grigolini

Rank-Size Distribution of Notes in Harmonic Music: Hierarchic Shuffling of Distributions

We trace the rank size distribution of notes in harmonic music, which on previous works we suggested was much better represented by the Two-parameter, first class Beta distribution than the customary power law, to the ranked mixing of distributions dictated by the harmonic and instrumental nature of the piece. The same representation is shown to arise in other fields by the same type of ranked shuffling of distributions. We include the codon content of intergenic DNA sequences and the ranked distribution of sizes of trees in a determined area as examples. We show that the fittings proposed increase their accuracy with the number of distributions that are mixed and ranked.

Manuel Beltrán del Río, Germinal Cocho

Dynamics of Priority-Queue Networks

In this Work-in-Progress paper, we study the dynamics of priority-queue networks by generalizing the interacting priority queue model of Oliveira and Vazquez [Physica A

388

, 187 (2009)]. We show that the original AND-type protocol for interacting tasks is not scalable for the queue networks with more than two queues. We then introduce a scalable interaction protocol, an OR-type one, and examine the effects of the number of queues and the network topology on the waiting time dynamics of the priority-queue networks. We also study the effect of synchronicity in task executions to the waiting time dynamics in the priority-queue networks.

Byung-Joon Min, Kwang-Il Goh, In-mook Kim

Generalized Thermodynamics Underlying the Laws of Zipf and Benford

We demonstrate that the laws of Zipf and Benford, that govern scores of data generated by many and diverse kinds of human activity (as well as other data from natural phenomena), are the centerpiece expressions of a generalized thermodynamic structure. This structure is obtained from a deformed type of statistical mechanics that arises when configurational phase space is incompletely visited in an especially severe fashion. Specifically, the restriction is that the accessible fraction of this space has fractal properties. We obtain a generalized version of Benford’s law for data expressed in full and not by the first digit. The inverse functional of this expression is identified with the Zipf’s law; but it naturally includes the tails observed in real data for small rank. Thermodynamically, our version of Benford’s law expresses a Legendre transform between two entropy (or Massieu) potentials, while Zipf’s law is merely the expression that relates the corresponding partition functions.

Carlo Altamirano, Alberto Robledo

The Main Principles of Simulation Modeling of the Sustainable Development Complexes System: Case of World Economy

The phenomenon of states changes of the world economy during the last 200 years shows that there is a certain 70-year regularity in its development, which is expressed in increased structural complexity of the global economic system every 70 years. The development happens after certain periods of bifurcation (up to 50 years) accompanied by the lower rates of economic development, and periods of adaptation (up to 20 years) with the higher rates. The theoretical justification of this process shows that the increased structural complexity of the global economic system is the external manifestations of the self-organization process in a large complex system we call the “world economy”. The “world economy” is regarded as global economic environment where countries and their group organizations are ordinary agents. Every agent has the same properties as the system: they can be open, non-equilibrium, dissipative, self-organizing; they can also have the aim - to maintain integrity through the main function (development). We can watch the fractal symmetry of all general properties ranging from the global system to its ordinary agent. Development, the main function of the system, is viewed as the movement of economic environment. Basing on the assumption about maintaining boundary limits of system stability we solve the task of stable movement of environment and sustainable development of the global civilization in the context of fixed main properties and system characteristics. On the basis of outlined properties we make a mathematical model of non-linear dynamic system - development of the global system.

Dmitry Chistilin

Towards the Characterization of Individual Users through Web Analytics

We perform an analysis of the way individual users navigate in the Web. We focus primarily in the temporal patterns of they return to a given page. The return probability as a function of time as well as the distribution of time intervals between consecutive visits are measured and found to be independent of the level of activity of single users. The results indicate a rich variety of individual behaviors and seem to preclude the possibility of defining a characteristic frequency for each user in his/her visits to a single site.

Bruno Gonçalves, José J. Ramasco

Control Mode of Public Emergency Response

Emergency is very difficult to be predicted since the social system has complex and comprehensive characters, so while a public emergency happens, a reasonable, efficient and timely response and control mode to be quickly selected is important to decrease the loss and to reduce the control cost. If the public emergency response agency doesn’t rapidly forecast or estimate the potential loss, an ineffective control mode would be adopted, and the emergency diffusion situation couldn’t be controlled, which would lead to the social instability. According to the different efficiency of response measure, the different control mode of public emergency response are classified into four types which are defined as lead-control mode, sync-control mode, delay-control mode and Invalid-control mode, respectively. The results show that the different cost is needed to control the emergency diffusion with different control mode, and the lead-control mode is the most efficient control model.

Ze-Meng Fan, Wen-Yuan Niu, Ji-Fa Gu

The Influence Factors and Mechanism of Societal Risk Perception

Risk perception is one of important subjects in management psychology and cognitive psychology. It is of great value in the theory and practice to investigate the societal hazards that the public cares a lot especially in Socio-economic transition period. A survey including 30 hazards and 6 risk attributes was designed and distributed to about 2, 485 residents of 8 districts, Beijing. The major findings are listed as following: Firstly, a scale of societal risk perception was designed and 2 factors were identified (Dread Risk & Unknown Risk). Secondly, structural equation model was used to analyze the influence factors and mechanism of societal risk perception. Risk preference, government support and social justice could influence societal risk perception directly. Government support fully moderated the relationship between government trust and societal risk perception. Societal risk perception influenced life satisfaction, public policy preferences and social development belief.

Rui Zheng, Kan Shi, Shu Li

Social Physics and the Flow of Migrant Peasant Workers

Social physics is an old and new discipline. In the seventeenth century, when physics as we know it was gradually taking shape, natural philosophers were attempting to apply the concepts and methods of statistical physics to society as a whole. The social physics is the use of natural way of thinking, the basic principles of universal rules, systems and methods of calculation means the social, economic and other complex issues of qualitative and quantitative analysis of complex subjects. In the direction of natural and social sciences Cross-full, it could be easy to understand the flow of migrant peasant workers preference choice of this complex issue.

Li Ding, Wang Yun-Lin

Social Physics and China’s Population Migration

Based on the social physics theory, this paper analyzes the economic disparities between different regions in China, and contributes a conceptual model of population migration among eastern, central, western and north-eastern regions. The national 1% population sample investigation data is adopted to build a network of inter-provincial population migration, and the population migration network is analyzed with social network analysis. The results are shown that there is a very strong correlation between migrant population and economy disparity in China, and the migration with obviously geographical characteristics. The eastern region is the main areas for migration-inflow; the central region is the main areas of migration-outflow; the western region is relatively “locked-up”, with a little of population flow; and the migration of the northeast is mainly within its own regional territory.

Yun-lin Wang, Ding Li

Social Combustion Theory: Dynamics of Social System Deterioration

Social Harmony Equation (SHE) leads the social system to the evolution direction of social by accumulation of “social combustion substances”, i.e., the accumulation of microcosmic entropy increase “basic particles” (individual) in social system from assimilated “basic social energy” to dissimilated one; meanwhile, the catalysis of “social combustion promoter” (social excitation energy) has enhanced the “social temperature” of disordering process of social system and completed the energy accumulation of social entropy increase that can generate the transition. Finally, ignited by the “social trigger threshold”, the social system has completed the abrupt change from orderliness to disorderliness. The continuous variation of the above-mentioned three basic non-linear processes has jointly composed the whole contents of social combustion theory. Under the restriction of such conditions of different time (t), different space (α) and different scale (β), it is finally explained as a comprehensive dynamics of social system deterioration.

Wen-yuan Niu

Research on the Best Time to Intervene into Network Public Opinion for Managers -Based on "Nankai Buick Affair"-

This study is based on network public opinion of the case - ”Nankai Buick Affair”. The daily number of posts as the variable, quantitative research is carried out on the law of change in network public opinion, which provides the scientific basis for managers to choose the best time to intervene. This article first begins from the three aspects: a number of changes in daily posts over time, the constitute of main topics and statistical analysis, and then gives a in-depth analysis of the law of change in network public opinion. After the emergency, performed by the number of posts, the shape of the rise and fall in network public opinion has three categories: sharp peak, severe right-skewed and fat tail. On this basis, then we study the changes of major issues in network public opinion in different stages: In the two periods: “Start- Peak” and “Peak-End”, not only the number of posts in BBS is not the same, but also the main topics in the BBS articles are different for the network public opinion of emergency. Finally, points out, If the managers issue truth of the incident in the prime time through the channels of authority, we can reduce the distance between the peak and the state of equilibrium for the network public opinion system, which is conducive to a smooth landing public opinion and social harmony.

Meiyang Chen, Yijun Liu

Research on Social Stability Mechanisms Based on Activation Energy and Gradual Activation Reaction Theory

This paper draws a comparison between social stability and chemical reaction process, and brings forward the concept of “social temperature” and “activation energy of social agent”. It is considered that social temperature turns out to be the macro symptom of social average energy, and its unceasing up-climbing roots in the energy accumulation of “inferiorization” process of social system; that “activation energy of social agent” stands for the social energy or temperature where individuals or groups reach the limit of their psychological bearing ability. This paper, basing on above concepts, elaborates on and demonstrates the gradual activation reaction mechanisms of social stability by a lot of concrete examples. It is thought that there is a threshold value for social stability, and the society will be unstable if social temperature goes higher than this value; that the larger the social average activation energy is, the higher the temperature threshold value of social stability will be; and considering that different groups have different activation energy, those fragile groups with low activation energy are often the risk source which might pose a threat to social stability.

Miao Ning, Jifa Gu

Research on Early Warning of Chinese Food Safety Based on Social Physics

Based on social physics, this paper designs the index system of food safety, builds early warning model of food safety, calculates the degree of food safety, and assesses the state of early warning of 2007 in China. The result shows the degree of food safety is near 0.7 in securer state, belonging to slight emergency. It is much lower in eastern areas of developed regions, belonging to insecure state in the mass. That the food safety is ensured in major grain producing areas, Inner Mongolia, Ningxia and Xinjiang is the prerequisite of realizing the food safety of China. The result also shows four significant indices, grain production capacity, grain circulation order, grain demand and grain supply, which are important indicatio to control food safety.

Yonghuan Ma, Wenyuan Niu, Qianqian Li

Qualitative Meta-synthesis Techniques for Analysis of Public Opinions for in-depth Study

Public opinions toward social concerns are worth to be noticed for decision makers to undertake any policies and their outcomes. Social psychologists usually undertake a series of investigations to access the interaction underneath the common thinking or actions. The design and statistic processing of the questionnaires always require a lot of manpower. In this paper, two technologies, denoted as CorMap and iView, for qualitative meta-synthesis which aims to acquire and extract assumptions, hypothesis or even just common grounds for further investigation, are applied to a free-association test on social risk.

Xijin Tang

Opinion Modeling Based on Meta-synthesis Approach

Harmonious opinion is indispensable to harmonious society. Opinion can be used as the benchmark or wind vane to judge social stability and harmony. Understanding and capturing the essential mechanism of opinion formation and diffusion will provide help for early forecasting and macro-regulation. Society is an open complex giant system (OCGS), in which controlling social opinion is a more complex system engineering project. This article firstly investigates various models for the dynamics of opinions from different perspectives. Next, combined concept modeling as a qualitative analysis method with multi-agent modeling as a quantitative simulation tool based on meta-synthesis approach is applied to the social opinion on exploring its essential mechanism. Finally, some concluding remarks and future works are given.

Yijun Liu

Expert Mining for Solving Social Harmony Problems

Social harmony problems are being existed in social system, which is an open giant complex system. For solving such kind of problems the Meta-synthesis system approach proposed by Qian XS et al will be applied. In this approach the data, information, knowledge, model, experience and wisdom should be integrated and synthesized. Data mining, text mining and web mining are good techniques for using data, information and knowledge. Model mining, psychology mining and expert mining are new techniques for mining the idea, opinions, experiences and wisdom. In this paper we will introduce the expert mining, which is based on mining the experiences, knowledge and wisdom directly from experts, managers and leaders.

Jifa Gu, Wuqi Song, Zhengxiang Zhu, Yijun Liu

Two-Dimensional Coupling Model on Social Deprivation and Its Application

This paper qualitatively describes the deprivation under different coupling situations of two-dimensional indicators and then establishes the two-dimensional coupling model on social deprivation, using the social welfare function approach and Foster-Greer-Thorbecke

P

α

method. Finally, this paper applies the model to evaluate the social deprivation of 31 provinces in China under the coupling state of capita disposable income and housing price.

Yun Fu

Internal-Evolution Driven Growth in Creation-Annihilation Cyclic Games

In this paper, the domain growth process in a novel kind of cyclic game is investigated by similation method. Different with the classical cyclic games, this process is called ”creation-annihilation process”, in which is just like the autocatalysis system. The results of numerical simulations show that the domain growth in such cyclic games with four or five states has a special feature: the growing domain usually has a stable boundary, and the growth is driven by the internal-evolution of the domain. Considering with the widespread of the cyclic autocatalysis in organism activities, such internal-evolution driven growth could be universal in many organism systems.

Xiao-Pu Han, Luo-Luo Jiang, Tao Zhou, Bing-Hong Wang

Immunization of Geographical Networks

We numerically investigate the epidemic spread phenomena and efficient immunization strategies on complex networks embedded in geometry. It is assumed that there exists an unavoidable time delay (we call it the detection time) between the actual infection and the beginning of immunization, and we implement two different immunization strategies: one is based on topological connection neighbors (CN) of the infected vertex and the other on geographical spatial neighbors (SN). It is found that the decrease of the detection time is very important for a successful immunization. Our results suggest that within the limitation of the network models considered here, in which the infection probability is assumed to decrease with the geographic distance, the simple SN strategy works almost equally or better than the CN strategy, especially when the detection time is longer.

Bing Wang, Kazuyuki Aihara, Beom Jun Kim

Stabilities of Stock States in Chinese Stock Markets

We study the evolution of the correlation-based clusters of stocks, which usually accord with business groups. By segmenting the whole time series into several overlapping segments, we trace the dynamical evolution of each business sectors in terms of the multi-factor model and especially treat the stock prices of Shanghai composites that are not incorporated into developed markets of the financial time stock exchange index.

Gyuchang Lim, Kyungho Seo, Soo Yong Kim, Kyungsik Kim

A Priority Queue Model of Human Dynamics with Bursty Input Tasks

The physics of human activities recently has been studied in the view point that they are dynamic processes of a complex system. The studies reveal that the human activities have bursty nature – occasional abrupt bursts of activity level for short periods of time, along with long periods of inactivity. Quantitative studies show that the distribution of the time,

τ

, between two consecutive activity events exhibits a power-law behavior with universal exponents ~

τ

− 1.5

or ~

τ

− 1.0

. Such universal behaviors were explained by the universality in the waiting-time distribution of tasks in model queue systems, which operate based on priority. In the models, the rates of task input are presumed to follow a Poisson-type distribution. An empirical observation of human activities, however, shows that the task arriving rate for some people also has bursty nature – the number of tasks arrive to the people follows a power-law distribution. In this paper, a new model queue system for this case is introduced and studied by analytic and numerical methods. The waiting-time distribution for the new model is found also to follow a power law, but the exponent varies according to the parameters of the model and takes other values than 1.5 or 1.0. The analytic solution is obtained via the generating function formalism, different from the biased random walk approach used in the previous studies.

Jin Seop Kim, Naoki Masuda, Byungnam Kahng

Modelling Uncertainty of Behaviour of Complex Economic System

Property relation management of the (ownership, disposable, using) limited resources, which naturally occurs in the economic systems, face a problem uncertainty behavior of its active elements.

The model of identification and forecasting of the economic system trajectory states in time is offered in work, which allows complex estimation its conduct from positions of risk (additive distributing), incompleteness (subadditive distributing) and contradiction (superadditive distributing) of information accessible to the manager.

Konstantin Kovalchuk

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