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Retrospective Relatedness Reconstruction: Applications to Adaptive Social Networks and Social Sentiment

Examination of temporally changing adaptive social networks has been difficult given the need for extensive and usually real-time data collection. Building from interdisciplinary advances, the authors propose a web search engine–based method (called retrospective relatedness reconstruction or 3R) for collecting approximated historical data of temporally changing adaptive social networks. As quantifying relatedness among people in social networks leads to difficulty in assigning proper weights to relationship ties, 3R offers a means for assessing relatedness between people over time. Additionally, 3R can be applied beyond people relatedness to include word associations. To illustrate these two novel contributions, the authors reconstructed the temporal evolution of a social network from 2005 to 2009 of 92 individuals (key leaders) related to the U.S. financial crisis and also examined the temporal evolution of social sentiment (i.e., fear, shame, blame, confidence) related to the same 92 individuals. We found several illustrative cases where temporal changes in centrality and/or sentiment captured actual events related to these individuals during this time period.

 

Retrospective Relatedness Reconstruction: Applications to Adaptive Social Networks and Social Sentiment
Shelley D. Dionne, et al.

Organizational Research Methods May 2, 2012

http://dx.doi.org/10.1177/1094428112442572

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Identification of core-periphery structure in networks

Many networks can be usefully decomposed into a dense core plus an outlying, loosely-connected periphery. Here we propose an algorithm for performing such a decomposition on empirical network data using methods of statistical inference. Our method fits a generative model of core-periphery structure to observed data using a combination of an expectation--maximization algorithm for calculating the parameters of the model and a belief propagation algorithm for calculating the decomposition itself. We find the method to be efficient, scaling easily to networks with a million or more nodes and we test it on a range of networks, including real-world examples as well as computer-generated benchmarks, for which it successfully identifies known core-periphery structure with low error rate. We also demonstrate that the method is immune from the detectability transition observed in the related community detection problem, which prevents the detection of community structure when that structure is too weak. There is no such transition for core-periphery structure, which is detectable, albeit with some statistical error, no matter how weak it is.


Identification of core-periphery structure in networks
Xiao Zhang, Travis Martin, M. E. J. Newman

http://arxiv.org/abs/1409.4813

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Exclusive Queueing Processes and their Application to Traffic Systems

The dynamics of pedestrian crowds has been studied intensively in recent years, both theoretically and empirically. However, in many situations pedestrian crowds are rather static, e.g. due to jamming near bottlenecks or queueing at ticket counters or supermarket checkouts. Classically such queues are often described by the M/M/1 queue that neglects the internal structure (density profile) of the queue by focussing on the system length as the only dynamical variable. This is different in the Exclusive Queueing Process (EQP) in which the queue is considered on a microscopic level. It is equivalent to a Totally Asymmetric Exclusion Process (TASEP) of varying length. The EQP has a surprisingly rich phase diagram with respect to the arrival probability alpha and the service probability beta. The behavior on the phase transition line is much more complex than for the TASEP with a fixed system length. It is nonuniversal and depends strongly on the update procedure used. In this article, we review the main properties of the EQP. We also mention extensions and applications of the EQP and some related models.


Exclusive Queueing Processes and their Application to Traffic Systems
C. Arita, A. Schadschneider

http://arxiv.org/abs/1409.4731

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Fractional dynamics on networks: Emergence of anomalous diffusion and Lévy flights

We introduce a formalism of fractional diffusion on networks based on a fractional Laplacian matrix that can be constructed directly from the eigenvalues and eigenvectors of the Laplacian matrix. This fractional approach allows random walks with long-range dynamics providing a general framework for anomalous diffusion and navigation, and inducing dynamically the small-world property on any network. We obtained exact results for the stationary probability distribution, the average fractional return probability, and a global time, showing that the efficiency to navigate the network is greater if we use a fractional random walk in comparison to a normal random walk. For the case of a ring, we obtain exact analytical results showing that the fractional transition and return probabilities follow a long-range power-law decay, leading to the emergence of Lévy flights on networks. Our general fractional diffusion formalism applies to regular, random, and complex networks and can be implemented from the spectral properties of the Laplacian matrix, providing an important tool to analyze anomalous diffusion on networks.


Fractional dynamics on networks: Emergence of anomalous diffusion and Lévy flights
Phys. Rev. E 90, 032809 – Published 17 September 2014
A. P. Riascos and José L. Mateos

http://dx.doi.org/10.1103/PhysRevE.90.032809

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A Neuroscientist’s Radical Theory of How Networks Become Conscious

A Neuroscientist’s Radical Theory of How Networks Become Conscious | Papers | Scoop.it
It's a question that's perplexed philosophers for centuries and scientists for decades: Where does consciousness come from? Neuroscientist Christof Koch, chief scientific officer at the Allen Institute for Brain Science, thinks he has an answer.

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The Time Scale of Evolutionary Innovation

Evolutionary adaptation can be described as a biased, stochastic walk of a population of sequences in a high dimensional sequence space. The population explores a fitness landscape. The mutation-selection process biases the population towards regions of higher fitness. In this paper we estimate the time scale that is needed for evolutionary innovation. Our key parameter is the length of the genetic sequence that needs to be adapted. We show that a variety of evolutionary processes take exponential time in sequence length. We propose a specific process, which we call ‘regeneration processes’, and show that it allows evolution to work on polynomial time scales. In this view, evolution can solve a problem efficiently if it has solved a similar problem already.


Chatterjee K, Pavlogiannis A, Adlam B, Nowak MA (2014) The Time Scale of Evolutionary Innovation. PLoS Comput Biol 10(9): e1003818. http://dx.doi.org/10.1371/journal.pcbi.1003818

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Symbolic regression of generative network models

Symbolic regression of generative network models | Papers | Scoop.it

Networks are a powerful abstraction with applicability to a variety of scientific fields. Models explaining their morphology and growth processes permit a wide range of phenomena to be more systematically analysed and understood. At the same time, creating such models is often challenging and requires insights that may be counter-intuitive. Yet there currently exists no general method to arrive at better models. We have developed an approach to automatically detect realistic decentralised network growth models from empirical data, employing a machine learning technique inspired by natural selection and defining a unified formalism to describe such models as computer programs. As the proposed method is completely general and does not assume any pre-existing models, it can be applied “out of the box” to any given network. To validate our approach empirically, we systematically rediscover pre-defined growth laws underlying several canonical network generation models and credible laws for diverse real-world networks. We were able to find programs that are simple enough to lead to an actual understanding of the mechanisms proposed, namely for a simple brain and a social network.


Symbolic regression of generative network models
• Telmo Menezes & Camille Roth

Scientific Reports 4, Article number: 6284 http://dx.doi.org/10.1038/srep06284

See Also: https://github.com/telmomenezes/synthetic

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Flora Moon's curator insight, September 14, 2:13 PM

Big data meets systems and can potentially shines a light on system dynamics....

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Dynamic Homeostasis in Packet Switching Networks

In this study, we investigate the adaptation and robustness of a packet switching network (PSN), the fundamental architecture of the Internet. We claim that the adaptation introduced by a transmission control protocol (TCP) congestion control mechanism is interpretable as the self-organization of multiple attractors and stability to switch from one attractor to another. To discuss this argument quantitatively, we study the adaptation of the Internet by simulating a PSN using ns-2. Our hypothesis is that the robustness and fragility of the Internet can be attributed to the inherent dynamics of the PSN feedback mechanism called the congestion window size, or \textit{cwnd}. By varying the data input into the PSN system, we investigate the possible self-organization of attractors in cwnd temporal dynamics and discuss the adaptability and robustness of PSNs. The present study provides an example of Ashby's Law of Requisite Variety in action.


Dynamic Homeostasis in Packet Switching Networks
Mizuki Oka, Hirotake Abe, Takashi Ikegami

http://arxiv.org/abs/1409.1533

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Emergence of multiplex mobile phone communication networks across rural areas: An Ethiopian experiment

Mobile phones are spreading to remote areas of the globe, leading to the following question: We have donated phones to 234 farmers selected by stratified random sampling in an agrarian region of Ethiopia and have tracked their main communication partners for six months. The panel data and qualitative interviews indicated that the phones were not typically used to expand the existing constrained social networks or to gain information from new sources but to call contacts who had been known personally and to individuals introduced through the experiment. Stochastic actor-based network models clarified that although agricultural information-seeking and casual calling are intertwined, the mechanisms underlying the evolution of instrumental and expressive communication networks are distinct. Acquaintances living beyond comfortable walking distances and individuals whom others call became preferred for information-seeking calls. Thus, mobile phones may accelerate information exchange within existing social networks and may support the creation of new information hubs that might facilitate more efficient information diffusion over long distances in the future. In contrast, the importance of geographical communities strongly prevails in casual phone conversations. Physically proximate community members who tend to be met frequently were preferred for sentiment-sharing calls. Preferential attachment was not evident for this type of communication. As a result, the network of these expressive calls was highly localized and fragmented, making it unlikely for personal feelings to diffuse across wide geographical areas through the new phone networks.


Emergence of multiplex mobile phone communication networks across rural areas: An Ethiopian experiment
PETR MATOUS, YASUYUKI TODO and TATSUYA ISHIKAWA

Network Science / Volume 2 / Issue 02 / August 2014, pp 162-188
http://dx.doi.org/10.1017/nws.2014.12

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A global strategy for road building

A global strategy for road building | Papers | Scoop.it

The number and extent of roads will expand dramatically this century. Globally, at least 25 million kilometres of new roads are anticipated by 2050; a 60% increase in the total length of roads over that in 2010. Nine-tenths of all road construction is expected to occur in developing nations, including many regions that sustain exceptional biodiversity and vital ecosystem services. Roads penetrating into wilderness or frontier areas are a major proximate driver of habitat loss and fragmentation, wildfires, overhunting and other environmental degradation, often with irreversible impacts on ecosystems. Unfortunately, much road proliferation is chaotic or poorly planned, and the rate of expansion is so great that it often overwhelms the capacity of environmental planners and managers. Here we present a global scheme for prioritizing road building. This large-scale zoning plan seeks to limit the environmental costs of road expansion while maximizing its benefits for human development, by helping to increase agricultural production, which is an urgent priority given that global food demand could double by mid-century. Our analysis identifies areas with high environmental values where future road building should be avoided if possible, areas where strategic road improvements could promote agricultural development with relatively modest environmental costs, and /`conflict areas/' where road building could have sizeable benefits for agriculture but with serious environmental damage. Our plan provides a template for proactively zoning and prioritizing roads during the most explosive era of road expansion in human history.


A global strategy for road building
• William F. Laurance, Gopalasamy Reuben Clements, Sean Sloan, Christine S. O’Connell, Nathan D. Mueller, Miriam Goosem, Oscar Venter, David P. Edwards, Ben Phalan, Andrew Balmford, Rodney Van Der Ree & Irene Burgues Arrea

Nature 513, 229–232 (11 September 2014) Nature 513, 229–232 (11 September 2014) doi:10.1038/nature1371710.1038/nature13717

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Primacy and ranking of UEFA soccer teams from biasing organization rules

A question is raised on whether some implied regularity or structure, as found in the soccer team ranking by the Union of European Football Associations (UEFA), is due to an implicit game result value or score competition conditions. The analysis is based on considerations of complex systems, i.e. finding whether power or other simple law fits are appropriate to describe some internal dynamics. It is observed that the ranking is specifically organized: a major class comprising a few teams emerges after each season. Other classes, which apparently have regular sizes, occur subsequently. Thus, the notion of the Sheppard primacy index is envisaged to describe the findings. Additional primacy indices are discussed for enhancing the features. These measures can be used to sort out peer classes in more general terms. A very simplified toy model containing components of the UEFA ranking rules suggests that such peer classes are an extrinsic property of the ranking, as obtained in many nonlinear systems under boundary condition constraints.


Primacy and ranking of UEFA soccer teams from biasing organization rules

Marcel Ausloos et al 2014 Phys. Scr. 89 108002 http://dx.doi.org/10.1088/0031-8949/89/10/108002

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Ten Simple Rules for Better Figures

Rule 1: Know Your Audience
Rule 2: Identify Your Message
Rule 3: Adapt the Figure to the Support Medium
Rule 4: Captions Are Not Optional
Rule 5: Do Not Trust the Defaults
Rule 6: Use Color Effectively
Rule 7: Do Not Mislead the Reader
Rule 8: Avoid “Chartjunk”
Rule 9: Message Trumps Beauty
Rule 10: Get the Right Tool


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The Leverage Effect on Wealth Distribution in a Controllable Laboratory Stock Market

The Leverage Effect on Wealth Distribution in a Controllable Laboratory Stock Market | Papers | Scoop.it

Wealth distribution has always been an important issue in our economic and social life, since it affects the harmony and stabilization of the society. Under the background of widely used financial tools to raise leverage these years, we studied the leverage effect on wealth distribution of a population in a controllable laboratory market in which we have conducted several human experiments, and drawn the conclusion that higher leverage leads to a higher Gini coefficient in the market. A higher Gini coefficient means the wealth distribution among a population becomes more unequal. This is a result of the ascending risk with growing leverage level in the market plus the diversified trading abilities and risk preference of the participants. This work sheds light on the effects of leverage and its related regulations, especially its impact on wealth distribution. It also shows the capability of the method of controllable laboratory markets which could be helpful in several fields of study such as economics, econophysics and sociology.

 

The Leverage Effect on Wealth Distribution in a Controllable Laboratory Stock Market

Zhu C, Yang G, An K, Huang J

PLoS ONE 9(6): e100681. (2014)

http://dx.doi.org/10.1371/journal.pone.0100681

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Self-organizing traffic signals using secondary extension and dynamic coordination

Self-organizing traffic signals using secondary extension and dynamic coordination | Papers | Scoop.it

Actuated traffic signal control logic has many advantages because of its responsiveness to traffic demands, short cycles, effective use of capacity leading to and recovering from oversaturation, and amenability to aggressive transit priority. Its main drawback has been its inability to provide good progression along arterials. However, the traditional way of providing progression along arterials, coordinated–actuated control with a common, fixed cycle length, has many drawbacks stemming from its long cycle lengths, inflexibility in recovering from priority interruptions, and ineffective use of capacity during periods of oversaturation. This research explores a new paradigm for traffic signal control, “self-organizing signals,” based on local actuated control but with some additional rules that create coordination mechanisms. The primary new rules proposed are for secondary extensions, in which the green may be held to serve an imminently arriving platoon, and dynamic coordination, in which small groups of closely spaced signals communicate with one another to cycle synchronously with the group’s critical intersection. Simulation tests in VISSIM performed on arterial corridors in Massachusetts and Arizona show overall delay reductions of up to 14% compared to an optimized coordinated–actuated scheme where there is no transit priority, and more than 30% in scenarios with temporary oversaturation. Tests also show that with self-organizing control, transit signal priority can be more effective than with coordinated–actuated control, reducing transit delay by about 60%, or 12 to 14 s per intersection with little impact on traffic delay.

Self-organizing traffic signals using secondary extension and dynamic coordination
Burak Cesme, Peter G. Furth
Transportation Research Part C: Emerging Technologies
Volume 48, November 2014, Pages 1–15
http://dx.doi.org/10.1016/j.trc.2014.08.006

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toyLIFE: a computational framework to study the multi-level organization of the genotype-phenotype map

The genotype-phenotype map is an essential object in our understanding of organismal complexity and adaptive properties, determining at once genomic plasticity and those constraints that may limit the ability of genomes to attain evolutionary innovations. An exhaustive experimental characterization of the relationship between genotypes and phenotypes is at present out of reach. Therefore, several models mimicking that map have been proposed and investigated, leading to the identification of a number of general features: genotypes differ in their robustness to mutations, phenotypes are represented by a broadly varying number of genotypes, and simple point mutations seem to suffice to navigate the space of genotypes while maintaining a phenotype. However, most current models address only one level of the map (sequences and folded structures in RNA or proteins; networks of genes and their dynamical attractors; sets of chemical reactions and their ability to undergo molecular catalysis), such that many relevant questions cannot be addressed. Here we introduce toyLIFE, a multi-level model for the genotype-phenotype map based on simple genomes and interaction rules from which a complex behavior at upper levels emerges, remarkably plastic gene regulatory networks and metabolism. toyLIFE is a tool that permits the investigation of how different levels are coupled, in particular how and where do mutations affect phenotype or how the presence of certain metabolites determines the dynamics of toyLIFE gene regulatory networks. The possibilities of this model are not exhausted by the results presented in this contribution. It can be easily generalized to incorporate evolution through mutations that change genome length or through recombination, to consider gene duplication or deletion, and therefore to explore further properties of extended genotype-phenotype maps.


toyLIFE: a computational framework to study the multi-level organization of the genotype-phenotype map
Clemente F. Arias, Pablo Catalán, Susanna Manrubia, José A. Cuesta

http://arxiv.org/abs/1409.4904

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Random Walks with Preferential Relocations to Places Visited in the Past and their Application to Biology

Strongly non-Markovian random walks offer a promising modeling framework for understanding animal and human mobility, yet, few analytical results are available for these processes. Here we solve exactly a model with long range memory where a random walker intermittently revisits previously visited sites according to a reinforced rule. The emergence of frequently visited locations generates very slow diffusion, logarithmic in time, whereas the walker probability density tends to a Gaussian. This scaling form does not emerge from the central limit theorem but from an unusual balance between random and long-range memory steps. In single trajectories, occupation patterns are heterogeneous and have a scale-free structure. The model exhibits good agreement with data of free-ranging capuchin monkeys.


Random Walks with Preferential Relocations to Places Visited in the Past and their Application to Biology
Phys. Rev. Lett. 112, 240601 – Published 18 June 2014
Denis Boyer and Citlali Solis-Salas

http://dx.doi.org/10.1103/PhysRevLett.112.240601


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Disease-Induced Resource Constraints Can Trigger Explosive Pandemic

Epidemic spreading is a complex process influenced by the intrinsic properties of a disease, the infrastructure that supports global economic and cultural exchange, and the behavior of individuals. Advances in mathematical epidemiology and network science have led to a better understanding of the risks posed by epidemic spreading and informed containment strategies such as immunization and treatment. However, a challenge that has been typically overlooked is that, as a disease becomes more prevalent, the burden that it places on capital can limit the supply of treatment. Here we study the dynamics of an epidemic when the recovery of sick individuals depends on the availability of healing resources that are generated by the healthy population. We find that epidemics spiral out of control into "explosive'' pandemics if the cost of recovery is above a critical cost. This occurs even when the standard models predict that the infection will not lead to an epidemic. Furthermore, the onset of explosive pandemics is very sudden, and we show through simulations and a mean-field analytical solution that the transition is always discontinuous, independent of the specific structure of the networks of human interaction through which the disease propagates.


Disease-Induced Resource Constraints Can Trigger Explosive Pandemics

Lucas Bottcher,

Olivia Woolley-Meza, Nuno A.M. Araujo, Hans J. Herrmann, Dirk Helbing


http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2496128

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How structurally stable are global socioeconomic systems?

The stability analysis of socioeconomic systems has been centred on answering whether small perturbations when a system is in a given quantitative state will push the system permanently to a different quantitative state. However, typically the quantitative state of socioeconomic systems is subject to constant change. Therefore, a key stability question that has been under-investigated is how strongly the conditions of a system itself can change before the system moves to a qualitatively different behaviour, i.e. how structurally stable the systems is. Here, we introduce a framework to investigate the structural stability of socioeconomic systems formed by a network of interactions among agents competing for resources. We measure the structural stability of the system as the range of conditions in the distribution and availability of resources compatible with the qualitative behaviour in which all the constituent agents can be self-sustained across time. To illustrate our framework, we study an empirical representation of the global socioeconomic system formed by countries sharing and competing for multinational companies used as proxy for resources. We demonstrate that the structural stability of the system is inversely associated with the level of competition and the level of heterogeneity in the distribution of resources. Importantly, we show that the qualitative behaviour of the observed global socioeconomic system is highly sensitive to changes in the distribution of resources. We believe that this work provides a methodological basis to develop sustainable strategies for socioeconomic systems subject to constantly changing conditions.


How structurally stable are global socioeconomic systems?
Serguei Saavedra, Rudolf P. Rohr, Luis J. Gilarranz, Jordi Bascompte

http://dx.doi.org/10.1098/ rsif.2014.0693
J. R. Soc. Interface 6 November 2014 vol. 11 no. 100 20140693

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Eli Levine's curator insight, September 14, 5:18 PM

There are most likely a plurality of stable socio-economic systems with different dynamics and levels of short term system stability.  It's likely that, even if there are periods of short term instability, that long term stability will hold, even if instability is a stable feature. 

 

Very interesting points here. 

 

Enjoy! 

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The state of global health in 2014

The global health landscape looks more promising than ever, although progress has been uneven. Here, we describe the current global burden of disease throughout the life cycle, highlighting regional differences in the unfinished agenda of communicable diseases and reproductive, maternal, and child health and the additive burden of emerging noncommunicable diseases and injuries. Understanding this changing landscape is an essential starting point for effective allocation of both domestic and international resources for health.


The state of global health in 2014
Jaime Sepúlveda, Christopher Murray

Science 12 September 2014:
Vol. 345 no. 6202 pp. 1275-1278
http://dx.doi.org/10.1126/science.1257099

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How memory generates heterogeneous dynamics in temporal networks

Empirical temporal networks display strong heterogeneities in their dynamics, which profoundly affect processes taking place on these networks, such as rumor and epidemic spreading. Despite the recent wealth of data on temporal networks, little work has been devoted to the understanding of how such heterogeneities can emerge from microscopic mechanisms at the level of nodes and links. Here we show that long-term memory effects are present in the creation and disappearance of links in empirical networks. We thus consider a simple generative modeling framework for temporal networks able to incorporate these memory mechanisms. This allows us to study separately the role of each of these mechanisms in the emergence of heterogeneous network dynamics. In particular, we show analytically and numerically how heterogeneous distributions of contact durations, of inter-contact durations and of numbers of contacts per link emerge. We also study the individual effect of heterogeneities on dynamical processes, such as the paradigmatic Susceptible-Infected epidemic spreading model. Our results confirm in particular the crucial role of the distributions of inter-contact durations and of the numbers of contacts per link.


How memory generates heterogeneous dynamics in temporal networks
Christian L. Vestergaard, Mathieu Génois, Alain Barrat

http://arxiv.org/abs/1409.1805

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Changing behaviors to prevent noncommunicable diseases

Noncommunicable diseases (NCDs)—largely comprising cardiovascular disease, cancer, chronic pulmonary disease, and diabetes—are the most important global health issues of the 21st century, as measured by both mortality and morbidity. These diseases are as preventable across entire populations as are infectious diseases, but require a different approach—one that involves policy change to promote healthy behaviors.


Changing behaviors to prevent noncommunicable diseases
Dave A. Chokshi, Thomas A. Farley

Science 12 September 2014:
Vol. 345 no. 6202 pp. 1243-1244
http://dx.doi.org/10.1126/science.1259809

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Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data

In this paper, we present a novel approach to predict crime in a geographic space from multiple data sources, in particular mobile phone and demographic data. The main contribution of the proposed approach lies in using aggregated and anonymized human behavioral data derived from mobile network activity to tackle the crime prediction problem. While previous research efforts have used either background historical knowledge or offenders' profiling, our findings support the hypothesis that aggregated human behavioral data captured from the mobile network infrastructure, in combination with basic demographic information, can be used to predict crime. In our experimental results with real crime data from London we obtain an accuracy of almost 70% when predicting whether a specific area in the city will be a crime hotspot or not. Moreover, we provide a discussion of the implications of our findings for data-driven crime analysis.


Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data
Andrey Bogomolov, Bruno Lepri, Jacopo Staiano, Nuria Oliver, Fabio Pianesi, Alex Pentland

http://arxiv.org/abs/1409.2983

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Dynamics of Media Attention

Studies of human attention dynamics analyses how attention is focused on specific topics, issues or people. In online social media, there are clear signs of exogenous shocks, bursty dynamics, and an exponential or powerlaw lifetime distribution. We here analyse the attention dynamics of traditional media, focussing on co-occurrence of people in newspaper articles. The results are quite different from online social networks and attention. Different regimes seem to be operating at two different time scales. At short time scales we see evidence of bursty dynamics and fast decaying edge lifetimes and attention. This behaviour disappears for longer time scales, and in that regime we find Poissonian dynamics and slower decaying lifetimes. We propose that a cascading Poisson process may take place, with issues arising at a constant rate over a long time scale, and faster dynamics at a shorter time scale.


Dynamics of Media Attention
V.A. Traag, R. Reinanda, J. Hicks, G. van Klinken

http://arxiv.org/abs/1409.2973

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The dilemma of statistics: Rigorous mathematical methods cannot compensate messy interpretations and lousy data

Statistics, although being indispensable in present day science and society has a bad reputation, in particular, in public. This can hardly be expressed in a better way than in the famous well-known quotation:
There are three kinds of lies: Lies, damned lies, and statistics. 1
It would be unfair not to make an attempt to restore the image of statistics and I try to do this in part by means of another citation.
While it is easy to lie with statistics, it is even easier to lie without them.
This quote is attributed to Frederick Mosteller [1]. Both citations are built undoubtedly on the association of statistics with telling lies and it is worth asking why statisticians have such a bad image. I feel there are two main reasons for it: (...)


The dilemma of statistics: Rigorous mathematical methods cannot compensate messy interpretations and lousy data
Peter Schuster
http://dx.doi.org/10.1002/cplx.21553

Complexity
Volume 20, Issue 1, pages 11–15, September/October 2014

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Probing and shaping the information transfer of noise-perturbed complex networks via Markov chain analysis

We demonstrate a recursive computational procedure based on the distributions of first passage time on Markov Chains that can mathematically characterize noise-driven processes in complex networks. Considering examples of both real (Enron email) and artificial (Ravasz-Barabasi) networks perturbed by noise using Monte Carlo simulations, our method accurately recovers the percentages that information will be transferred to the intended receivers. The paradigm reported here captures and provides explanation to the recent results of Czaplicka et al (Nature Sci. Rep. 2013) showing that the presence of noise can actually enhance the transfer of information in a hierarchical complex network. Finally, we illustrate how adaptive thresholding guided by our developed procedure can be used to engineer or shape the dynamic range of networks operating in a noisy environment.

 

Probing and shaping the information transfer of noise-perturbed complex networks via Markov chain analysis

MA Ramli, C Monterola

Journal of Computational Science, Available online 23 August 2014

http://dx.doi.org/10.1016/j.jocs.2014.08.002

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Emergence of Criticality in the Transportation Passenger Flow: Scaling and Renormalization in the Seoul Bus System

Emergence of Criticality in the Transportation Passenger Flow: Scaling and Renormalization in the Seoul Bus System | Papers | Scoop.it

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 power-law correlations in the system, that is, power-law 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.

 

Emergence of Criticality in the Transportation Passenger Flow: Scaling and Renormalization in the Seoul Bus System

Goh S, Lee K, Choi M, Fortin J-Y 

PLoS ONE 9(3): e89980. (2014) 

http://dx.doi.org/10.1371/journal.pone.0089980

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