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This chapter introduces statistical methods used in the analysis of social networks and in the rapidly evolving parallelfield of network science. Although several instances of social network analysis in health services research have appeared recently, the majority involve only the most basic methods and thus scratch the surface of what might be accomplished. Cuttingedge methods using relevant examples and illustrations in health services research are provided. by A. James O'Malley, JukkaPekka Onnela arXiv:1404.0067 [physics.socph]
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Our empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes. Batool K, Niazi MA (2014) Towards a Methodology for Validation of Centrality Measures in Complex Networks. PLoS ONE 9(4): e90283. http://dx.doi.org/10.1371/journal.pone.0090283
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We give a tutorial for the study of dynamical systems on networks, and we focus in particular on ``simple" situations that are tractable analytically. We briefly motivate why examining dynamical systems on networks is interesting and important. We then give several fascinating examples and discuss some theoretical results. We also discuss dynamical systems on dynamical (i.e., timedependent) networks, overview software implementations, and give our outlook on the field.
Via Bernard Ryefield
Similar patterns of interaction, such as network motifs and feedback loops, are used in many natural collective processes, probably because they have evolved independently under similar pressures. Here I consider how three environmental constraints may shape the evolution of collective behavior: the patchiness of resources, the operating costs of maintaining the interaction network that produces collective behavior, and the threat of rupture of the network. The ants are a large and successful taxon that have evolved in very diverse environments. Examples from ants provide a starting point for examining more generally the fit between the particular pattern of interaction that regulates activity, and the environment in which it functions. Gordon DM (2014) The Ecology of Collective Behavior. PLoS Biol 12(3): e1001805. http://dx.doi.org/10.1371/journal.pbio.1001805
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In this paper we present a macroeconomic microfounded framework with heterogeneous agents—individuals, firms, banks—which interact through a decentralized matching process presenting common features across four markets—goods, labor, credit and deposit. We study the dynamics of the model by means of computer simulation. Some macroeconomic properties emerge such as endogenous business cycles, nominal GDP growth, unemployment rate fluctuations, the Phillips curve, leverage cycles and credit constraints, bank defaults and financial instability, and the importance of government as an acyclical sector which stabilize the economy. The model highlights that even extended crises can endogenously emerge. In these cases, the system may remain trapped in a large unemployment status, without the possibility to quickly recover unless an exogenous intervention takes place. by Luca Riccetti, Alberto Russo, Mauro Gallegati
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Social systems have recently attracted much attention, with attempts to understand social behavior with the aid of statistical mechanics applied to complex systems. Collective properties of such systems emerge from couplings between components, for example, individual persons, transportation nodes such as airports or subway stations, and administrative districts. Among various collective properties, criticality is known as a characteristic property of a complex system, which helps the systems to respond flexibly to external perturbations. This work considers the criticality of the urban transportation system entailed in the massive smart card data on the Seoul transportation network. Analyzing the passenger flow on the Seoul bus system during one week, we find explicit powerlaw correlations in the system, that is, powerlaw behavior of the strength correlation function of bus stops and verify scale invariance of the strength fluctuations. Such criticality is probed by means of the scaling and renormalization analysis of the modified gravity model applied to the system. Here a group of nearby (bare) bus stops are transformed into a (renormalized) “block stop” and the scaling relations of the network density turn out to be closely related to the fractal dimensions of the system, revealing the underlying structure. Specifically, the resulting renormalized values of the gravity exponent and of the Hill coefficient give a good description of the Seoul bus system: The former measures the characteristic dimensionality of the network whereas the latter reflects the coupling between distinct transportation modes. It is thus demonstrated that such ideas of physics as scaling and renormalization can be applied successfully to social phenomena exemplified by the passenger flow.
Via Bernard Ryefield
Social networks readily transmit information, albeit with less than perfect fidelity. We present a largescale measurement of this imperfect information copying mechanism by examining the dissemination and evolution of thousands of memes, collectively replicated hundreds of millions of times in the online social network Facebook. The information undergoes an evolutionary process that exhibits several regularities. A meme's mutation rate characterizes the population distribution of its variants, in accordance with the Yule process. Variants further apart in the diffusion cascade have greater edit distance, as would be expected in an iterative, imperfect replication process. Some text sequences can confer a replicative advantage; these sequences are abundant and transfer "laterally" between different memes. Subpopulations of the social network can preferentially transmit a specific variant of a meme if the variant matches their beliefs or culture. Understanding the mechanism driving change in diffusing information has important implications for how we interpret and harness the information that reaches us through our social networks. Information Evolution in Social Networks Lada A. Adamic, Thomas M. Lento, Eytan Adar, Pauline C. Ng http://arxiv.org/abs/1402.6792
Via Complexity Digest
PLOS ONE: an inclusive, peerreviewed, openaccess resource from the PUBLIC LIBRARY OF SCIENCE. Reports of wellperformed scientific studies from all disciplines freely available to the whole world.
Via Bryan Knowles, Bernard Ryefield
Two fundamental issues surrounding research on Zipf's law regarding city sizes are whether and why Zipf's law holds. This paper does not deal with the latter issue with respect to why, and instead investigates whether Zipf's law holds in a global setting, thus involving all cities around the world. Unlike previous studies, which have mainly relied on conventional census data, and census bureauimposed definitions of cities, we adopt naturally and objectively delineated cities, or natural cities, to be more precise, in order to examine Zipf's law. We find that Zipf's law holds remarkably well for all natural cities at the global level, and remains almost valid at the continental level except for Africa at certain time instants. We further examine the law at the country level, and note that Zipf's law is violated from country to country or from time to time. This violation is mainly due to our limitations; we are limited to individual countries, and to a static view on citysize distributions. The central argument of this paper is that Zipf's law is universal, and we therefore must use the correct scope in order to observe it. We further find that this law is reflected in the distribution of cities: the number of cities in individual countries follows an inverse power relationship; the number of cities in the first largest country is twice as many as that in the second largest country, three times as many as that in the third largest country, and so on. Zipf's Law for All the Natural Cities around the World Bin Jiang, Junjun Yin, Qingling Liu http://arxiv.org/abs/1402.2965
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The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean networks (RBNs) are commonly used as a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most realworld networks are spatially extended and arranged with regular, powerlaw, smallworld, or other nonrandom connections. Here we explore the RBN network topology between extreme local connections, random smallworld, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the Hamming distance at very low connectivities ($\bar{K} << 1$) and that the critical connectivity of stability $\bar{K}$ changes compared to random networks. At higher $\bar{K}$, this scaling remains unchanged. We also show that the Hamming distance of spatially local networks scales with a power law as the system size $N$ increases, but with a different exponent for local and smallworld networks. The scaling arguments for smallworld networks are obtained with respect to the system sizes and strength of spatially local connections. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design tradeoffs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics. Qiming Lu and Christof Teuscher Damage spreading in spatial and smallworld random Boolean networks Phys. Rev. E 89, 022806 (2014) http://pre.aps.org/abstract/PRE/v89/i2/e022806
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We address theoretically whether and under what conditions Schelling's celebrated result of 'selforganized' unintended residential segregation may also apply to school segregation. We propose here a computational model of school segregation that is aligned with a corresponding Schellingtype model of residential segregation. To adapt the model for application to school segregation, we move beyond previous work by combining two preference arguments in modeling parents' school choice, preferences for the ethnic composition of a school and preferences for minimizing the travelling distance to the school. In a set of computational experiments we assessed the effects of population composition and distance preferences in the school model. We found that a preference for nearby schools can suppress the trend towards selforganized segregation obtained in a baseline condition where parents were indifferent towards distance. We then investigated the joint effects of the variation of agents' 'tolerance' for outgroup members and distance preference. We found that integrated distributions were preserved under a much broader range of conditions than in the absence of a preference for nearby schools. We conclude that parents' preferences for nearby schools may be an important factor in tempering for school choice the segregation dynamics known from models of residential segregation. From Schelling to Schools: A Comparison of a Model of Residential Segregation with a Model of School Segregation Victor Ionut Stoica and Andreas Flache Journal of Artificial Societies and Social Simulation 17 (1) 5 http://jasss.soc.surrey.ac.uk/17/1/5.html ;
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This work is to honour Professor Peter M. Allen, a seminal figure in the foundation and development of Complexity Science in human systems. From before the time of his joining Nobel Prize winner Ilya Progogine's pioneering group at the Université libre de Bruxelles in 1967 Peter had started publishing on what was then known as Prigogine theory in physics. But it was only after this that his own pioneering work in Complexity Science showed the importance of its applications in evolutionary and human sciences. Since then he has been an influential and guiding figure in this field. The works collected are by admiring colleagues, friends and collaborators, all leaders in their fields, influenced by his seminal ides, and gathered from across a gamut of fields in human systems. This makes this a valuable and unique work, a veritable reader in the influence Complex Systems theory on a wide and diverse range of fields; from archaeology, city design, international banking, economics, policy studies and more.
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Understanding why some human populations remain persistently poor remains a significant challenge for both the social and natural sciences. The extremely poor are generally reliant on their immediate natural resource base for subsistence and suffer high rates of mortality due to parasitic and infectious diseases. Economists have developed a range of models to explain persistent poverty, often characterized as poverty traps, but these rarely account for complex biophysical processes. In this Essay, we argue that by coupling insights from ecology and economics, we can begin to model and understand the complex dynamics that underlie the generation and maintenance of poverty traps, which can then be used to inform analyses and possible intervention policies. To illustrate the utility of this approach, we present a simple coupled model of infectious diseases and economic growth, where poverty traps emerge from nonlinear relationships determined by the number of pathogens in the system. These nonlinearities are comparable to those often incorporated into poverty trap models in the economics literature, but, importantly, here the mechanism is anchored in core ecological principles. Coupled models of this sort could be usefully developed in many economically important biophysical systems—such as agriculture, fisheries, nutrition, and land use change—to serve as foundations for deeper explorations of how fundamental ecological processes influence structural poverty and economic development. Ngonghala CN, Pluciński MM, Murray MB, Farmer PE, Barrett CB, et al. (2014) Poverty, Disease, and the Ecology of Complex Systems. PLoS Biol 12(4): e1001827. http://dx.doi.org/10.1371/journal.pbio.1001827
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The modern world is complex beyond human understanding and control. The science of complex systems aims to find new ways of thinking about the many interconnected networks of interaction that defy traditional approaches. Thus far, research into networks has largely been restricted to pairwise relationships represented by links between two nodes. This volume marks a major extension of networks to multidimensional hypernetworks for modeling multielement relationships, such as companies making up the stock market, the neighborhoods forming a city, people making up committees, divisions making up companies, computers making up the internet, men and machines making up armies, or robots working as teams. This volume makes an important contribution to the science of complex systems by: (i) extending network theory to include dynamic relationships between many elements; (ii) providing a mathematical theory able to integrate multilevel dynamics in a coherent way; (iii) providing a new methodological approach to analyze complex systems; and (iv) illustrating the theory with practical examples in the design, management and control of complex systems taken from many areas of application.
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It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is far more complex. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of exposing messages on the userinterface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We provide a framework for unifying information visibility, divided attention, and explicit social feedback to predict the temporal dynamics of user behavior.
Via Shaolin Tan, NESS
Human conflict, geopolitical crises, terrorist attacks, and natural disasters can turn large parts of energy distribution networks offline. Europe's current gas supply network is largely dependent on deliveries from Russia and North Africa, creating vulnerabilities to social and political instabilities. During crises, less delivery may mean greater congestion, as the pipeline network is used in ways it has not been designed for. Given the importance of the security of natural gas supply, we develop a model to handle network congestion on various geographical scales. We offer a resilient response strategy to energy shortages and quantify its effectiveness for a variety of relevant scenarios. In essence, Europe's gas supply can be made robust even to major supply disruptions, if a fair distribution strategy is applied.
Via Claudia Mihai
The combination of the network theoretic approach with recently available abundant economic data leads to the development of novel analytic and computational tools for modelling and forecasting key economic indicators. The main idea is to introduce a topological component into the analysis, taking into account consistently all higherorder interactions. We present three basic methodologies to demonstrate different approaches to harness the resulting network gain. First, a multiple linear regression optimisation algorithm is used to generate a relational network between individual components of national balance of payment accounts. This model describes annual statistics with a high accuracy and delivers good forecasts for the majority of indicators. Second, an earlywarning mechanism for global financial crises is presented, which combines network measures with standard economic indicators. From the analysis of the crossborder portfolio investment network of longterm debt securities, the proliferation of a wide range of overthecountertraded financial derivative products, such as credit default swaps, can be described in terms of grossmarket values and notional outstanding amounts, which are associated with increased levels of market interdependence and systemic risk. Third, considering the flownetwork of goods traded between G20 economies, network statistics provide better proxies for key economic measures than conventional indicators. For example, it is shown that a country's gatekeeping potential, as a measure for local power, projects its annual change of GDP generally far better than the volume of its imports or exports. Netconomics: Novel Forecasting Techniques from the Combination of Big Data, Network Science and Economics Andreas Joseph, Irena Vodenska, Eugene Stanley, Guanrong Chen http://arxiv.org/abs/1403.0848
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Researchers, policymakers and law enforcement agencies across the globe struggle to find effective strategies to control criminal networks. The effectiveness of disruption strategies is known to depend on both network topology and network resilience. However, as these criminal networks operate in secrecy, datadriven knowledge concerning the effectiveness of different criminal network disruption strategies is very limited. By combining computational modeling and social network analysis with unique criminal network intelligence data from the Dutch Police, we discovered, in contrast to common belief, that criminal networks might even become ‘stronger’, after targeted attacks. On the other hand increased efficiency within criminal networks decreases its internal security, thus offering opportunities for law enforcement agencies to target these networks more deliberately. Our results emphasize the importance of criminal network interventions at an early stage, before the network gets a chance to (re)organize to maximum resilience. In the end disruption strategies force criminal networks to become more exposed, which causes successful network disruption to become a longterm effort. The Relative Ineffectiveness of Criminal Network Disruption Paul A. C. Duijn, Victor Kashirin & Peter M. A. Sloot Scientific Reports 4, Article number: 4238 http://dx.doi.org/10.1038/srep04238 ; See also documentary at http://www.youtube.com/watch?v=Qhk9ciHlzzo
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PLOS ONE: an inclusive, peerreviewed, openaccess resource from the PUBLIC LIBRARY OF SCIENCE. Reports of wellperformed scientific studies from all disciplines freely available to the whole world.
Via Bryan Knowles, Bernard Ryefield
We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are not effective and sufficient to contain them. The failure of many conventional approaches results from their neglection of feedback loops, instabilities and/or cascade effects, due to which equilibrium models do often not provide a good picture of the actual system behavior. However, the complex and often counterintuitive behavior of social systems and their macrolevel collective dynamics can be understood by means of complexity science, which enables one to address the aforementioned problems more successfully. We highlight that a suitable system design and management can help to stop undesirable cascade effects and to enable favorable kinds of selforganization in the system. In such a way, complexity science can help to save human lives.
Via Bernard Ryefield
Predictive Feedback Control is an easytoimplement method to stabilize unknown unstable periodic orbits in chaotic dynamical systems. Predictive Feedback Control is severely limited because asymptotic convergence speed decreases with stronger instabilities which in turn are typical for larger target periods, rendering it harder to effectively stabilize periodic orbits of large period. Here, we study stalled chaos control, where the application of control is stalled to make use of the chaotic, uncontrolled dynamics, and introduce an adaptation paradigm to overcome this limitation and speed up convergence. This modified control scheme is not only capable of stabilizing more periodic orbits than the original Predictive Feedback Control but also speeds up convergence for typical chaotic maps, as illustrated in both theory and application. The proposed adaptation scheme provides a way to tune parameters online, yielding a broadly applicable, fast chaos control that converges reliably, even for periodic orbits of large period. Controlling Chaos Faster Christian Bick, Christoph Kolodziejski, Marc Timme http://arxiv.org/abs/1402.4763
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Natural numbers can be divided in two nonoverlapping infinite sets, primes and composites, with composites factorizing into primes. Despite their apparent simplicity, the elucidation of the architecture of natural numbers with primes as building blocks remains elusive. Here, we propose a new approach to decoding the architecture of natural numbers based on complex networks and stochastic processes theory. We introduce a parameterfree nonMarkovian dynamical model that naturally generates random primes and their relation with composite numbers with remarkable accuracy. Our model satisfies the prime number theorem as an emerging property and a refined version of Cram\'er's conjecture about the statistics of gaps between consecutive primes that seems closer to reality than the original Cram\'er's version. Regarding composites, the model helps us to derive the prime factors counting function, giving the probability of distinct prime factors for any integer. Probabilistic models like ours can help not only to conjecture but also to prove results about primes and the complex architecture of natural numbers. The complex architecture of primes and natural numbers Guillermo GarciaPerez, M. Angeles Serrano, Marian Boguna http://arxiv.org/abs/1402.3612
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