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Robustness of skeletons and salient features in networks

Real world network datasets often contain a wealth of complex topological information. In the face of these data, researchers often employ methods to extract reduced networks containing the most important structures or pathways, sometimes known as skeletons' or backbones'. Numerous such methods have been developed. Yet data are often noisy or incomplete, with unknown numbers of missing or spurious links. Relatively little effort has gone into understanding how salient network extraction methods perform in the face of noisy or incomplete networks. We study this problem by comparing how the salient features extracted by two popular methods change when networks are perturbed, either by deleting nodes or links, or by randomly rewiring links. Our results indicate that simple, global statistics for skeletons can be accurately inferred even for noisy and incomplete network data, but it is crucial to have complete, reliable data to use the exact topologies of skeletons or backbones. These results also help us understand how skeletons respond to damage to the network itself, as in an attack scenario.

Robustness of skeletons and salient features in networks
Louis M. Shekhtman, James P. Bagrow, Dirk Brockmann

http://arxiv.org/abs/1309.3797

ComplexInsight's curator insight,

Very relevent to some current work  we are doing on data modeling and data mining -  Awesome scoop  - big thanks Eugene and Complexity Digest..

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Recent publications related to complex systems
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The unique ecology of human predators

Paradigms of sustainable exploitation focus on population dynamics of prey and yields to humanity but ignore the behavior of humans as predators. We compared patterns of predation by contemporary hunters and fishers with those of other predators that compete over shared prey (terrestrial mammals and marine fishes). Our global survey (2125 estimates of annual finite exploitation rate) revealed that humans kill adult prey, the reproductive capital of populations, at much higher median rates than other predators (up to 14 times higher), with particularly intense exploitation of terrestrial carnivores and fishes. Given this competitive dominance, impacts on predators, and other unique predatory behavior, we suggest that humans function as an unsustainable “super predator,” which—unless additionally constrained by managers—will continue to alter ecological and evolutionary processes globally.

The unique ecology of human predators
Chris T. Darimont, Caroline H. Fox, Heather M. Bryan, Thomas E. Reimchen

Science 21 August 2015:
Vol. 349 no. 6250 pp. 858-860
http://dx.doi.org/10.1126/science.aac4249&nbsp;

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Colloidal matter: Packing, geometry, and entropy

Colloidal particles, which consist of clusters of hundreds or thousands of atoms, can still resemble atomic systems. In particular, colloids have been used to study the packing of spheres and the influence of short-range interactions on crystallization and melting. Manoharan reviews these similarities, as well as the cases in which colloidal particles show behavior not seen in atomic systems. For example, the packing of nonspherical objects, where geometry or topology may matter, can give insights into the role of entropy in packing.

Colloidal matter: Packing, geometry, and entropy
Vinothan N. Manoharan

Science 28 August 2015:
Vol. 349 no. 6251
http://dx.doi.org/10.1126/science.1253751

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SMART DATA: Running the Internet of Things as a Citizen Web

Moore's law, describing the exponential explosion of processing power and data production, is currently driving a fundamental transformation of our economy and society. While processing power doubles every 18 months, data volumes double every 12 months, which means that we literally produce as much data in one year as in the entire history of humankind (i.e. all previous years). However, this is not the end of the digital revolution. More and more "things" are now equipped with communicating sensors - fridges, coffee machines, tooth brushes, smartphones and smart devices. In ten years, this will connect 150 billion "things" with each other - and with 10 billion people. This creates the "Internet of Everything" and data volumes that double every 12 hours rather than every 12 months. How will this impact our society?

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An Entangled Model for Sustainability Indicators

Nowadays the challenge for humanity is to find pathways towards sustainable development. Decision makers require a set of sustainability indicators to know if the sustainability strategies are following those pathways. There are more than one hundred sustainability indicators but they differ on their relative importance according to the size of the locality and change on time. The resources needed to follow these sustainability indicators are scarce and in some instances finite, especially in smaller regions. Therefore strategies to select set of these indicators are useful for decision makers responsible for monitoring sustainability. In this paper we propose a model for the identification and selection of a set of sustainability indicators that adequately represents human systems.

Vázquez P, del Río JA, Cedano KG, Martínez M, Jensen HJ (2015) An Entangled Model for Sustainability Indicators. PLoS ONE 10(8): e0135250. http://dx.doi.org/10.1371/journal.pone.0135250

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Fundamental limitations of network reconstruction

Network reconstruction helps us understand, diagnose and control complex networked systems by inferring properties of their interaction matrices, which characterize how nodes in the systems directly interact with each other. Despite a decade of extensive studies, network reconstruction remains an outstanding challenge. The fundamental limitations on which properties of the interaction matrix can be inferred from accessing the dynamics of individual nodes remain unknown. Here we characterize these fundamental limitations by deriving the necessary and sufficient condition to reconstruct any property of the interaction matrix. Counterintuitively, we prove that inferring less information ---such as the sign/connectivity pattern or the degree sequence--- does not make the network reconstruction problem easier than recovering the interaction matrix itself (i.e. the traditional parameter identification problem). Our analysis also reveals that using prior information of the interaction matrix ---such as bound on the edge-weights--- is the only way to circumvent these fundamental limitations of network reconstruction. This sheds light on designing new algorithms with practical improvements over parameter identification methods.

Fundamental limitations of network reconstruction
Marco Tulio Angulo, Jaime A. Moreno, Albert-László Barabási, Yang-Yu Liu

http://arxiv.org/abs/1508.03559

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Supersampling and Network Reconstruction of Urban Mobility

Understanding human mobility is of vital importance for urban planning, epidemiology, and many other fields that draw policies from the activities of humans in space. Despite the recent availability of large-scale data sets of GPS traces or mobile phone records capturing human mobility, typically only a subsample of the population of interest is represented, giving a possibly incomplete picture of the entire system under study. Methods to reliably extract mobility information from such reduced data and to assess their sampling biases are lacking. To that end, we analyzed a data set of millions of taxi movements in New York City. We first show that, once they are appropriately transformed, mobility patterns are highly stable over long time scales. Based on this observation, we develop a supersampling methodology to reliably extrapolate mobility records from a reduced sample based on an entropy maximization procedure, and we propose a number of network-based metrics to assess the accuracy of the predicted vehicle flows. Our approach provides a well founded way to exploit temporal patterns to save effort in recording mobility data, and opens the possibility to scale up data from limited records when information on the full system is required.

Sagarra O, Szell M, Santi P, Díaz-Guilera A, Ratti C (2015) Supersampling and Network Reconstruction of Urban Mobility. PLoS ONE 10(8): e0134508. http://dx.doi.org/10.1371/journal.pone.0134508 ;

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 Rescooped by Complexity Digest from Sustainable Complex Coevolutionary Systems Engineering

The collaborative roots of corruption

Recent financial scandals highlight the devastating consequences of corruption. While much is known about individual immoral behavior, little is known about the collaborative roots of curruption. In a novel experimental paradigm, people could adhere to one of two competing moral norms: collaborate vs. be honest. Whereas collaborative settings may boost honesty due to increased observability, accountability, and reluctance to force others to become accomplices, we show that collaboration, particularly on equal terms, is inductive to the emergence of corruption. When partners' profits are not aligned, or when individuals complete a comparable task alone, corruption levels drop. These findings reveal a dark side of collaboration, suggesting that human cooperative tendencies, and not merely greed, take part in shaping corruption.

The collaborative roots of corruption
Ori Weisela and Shaul Shalvi

PNAS

Via Dr Alejandro Martinez-Garcia
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The developmental dynamics of marmoset monkey vocal production

As human infants grow, their vocalizations change from cries, to babbles, to words. This pattern has been presumed to be absent from other primates. Indeed, the development of bird song is often regarded as a closer approximation of human language development. Takahashi et al., however, observed that marmoset cries and calls in the first 2 months after birth mature in much the same way as they do in humans (see the Perspective by Margoliash and Tchernichovski). Calls changed as the infants' vocal structures grew and were influenced by feedback from their parents.

The developmental dynamics of marmoset monkey vocal production
D. Y. Takahashi, A. R. Fenley, Y. Teramoto, D. Z. Narayanan, J. I. Borjon, P. Holmes, A. A. Ghazanfar

Science 14 August 2015:
Vol. 349 no. 6249 pp. 734-738
http://dx.doi.org/10.1126/science.aab1058

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Can randomized trials eliminate global poverty?

A new generation of economists is trying to transform global development policy through the power of randomized controlled trials.

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Field theory of molecular cooperators

It has been suggested that major transitions in evolution require the emergence of novelties, often associated to the cooperative behaviour of previously existing objects or agents. A key innovation involves the first cooperative interactions among molecules in a prebiotic biosphere. One of the simplest scenarios includes two molecular species capable of helping each other forming a catalytic loop or hypercycle. The second order kinetics of the hypercycle implies a hyperbolic growth dynamics, capable of overcoming some selection barriers associated to non-cooperative molecular systems. Moreover, it has been suggested that molecular replicators might have benefited from a limited diffusion associated to their attachment to surfaces: evolution and escape from extinction might have been tied to living on a surface. In this paper we propose a field theoretical model of the hypercycle involving reaction and diffusion through the use of a many-body Hamiltonian. This treatment allows a characterisation of the spatially correlated dynamics of the system, where the critical dimension is found to be d_c=2. We discuss the role of surface dynamics as a selective advantage for the system's survival.

Field theory of molecular cooperators
Jordi Piñero, Ricard Solé

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Evolution of Self-Organized Task Specialization in Robot Swarms

Many biological systems execute tasks by dividing them into finer sub-tasks first. This is seen for example in the advanced division of labor of social insects like ants, bees or termites. One of the unsolved mysteries in biology is how a blind process of Darwinian selection could have led to such highly complex forms of sociality. To answer this question, we used simulated teams of robots and artificially evolved them to achieve maximum performance in a foraging task. We find that, as in social insects, this favored controllers that caused the robots to display a self-organized division of labor in which the different robots automatically specialized into carrying out different subtasks in the group. Remarkably, such a division of labor could be achieved even if the robots were not told beforehand how the global task of retrieving items back to their base could best be divided into smaller subtasks. This is the first time that a self-organized division of labor mechanism could be evolved entirely de-novo. In addition, these findings shed significant new light on the question of how natural systems managed to evolve complex sociality and division of labor.

Ferrante E, Turgut AE, Duéñez-Guzmán E, Dorigo M, Wenseleers T (2015) Evolution of Self-Organized Task Specialization in Robot Swarms. PLoS Comput Biol 11(8): e1004273. http://dx.doi.org/10.1371/journal.pcbi.1004273 ;

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Taming Instabilities in Power Grid Networks by Decentralized Control

Renewables will soon dominate energy production in our electric power system. And yet, how to integrate renewable energy into the grid and the market is still a subject of major debate. Decentral Smart Grid Control (DSGC) was recently proposed as a robust and decentralized approach to balance supply and demand and to guarantee a grid operation that is both economically and dynamically feasible. Here, we analyze the impact of network topology by assessing the stability of essential network motifs using both linear stability analysis and basin volume for delay systems. Our results indicate that if frequency measurements are averaged over sufficiently large time intervals, DSGC enhances the stability of extended power grid systems. We further investigate whether DSGC supports centralized and/or decentralized power production and fi?nd it to be applicable to both. However, our results on cycle-like systems suggest that DSGC favors systems with decentralized production. Here, lower line capacities and lower averaging times are required compared to those with centralized production.

Taming Instabilities in Power Grid Networks by Decentralized Control
Benjamin Schäfer, Carsten Grabow, Sabine Auer, Jürgen Kurths, Dirk Witthaut, Marc Timme

http://arxiv.org/abs/1508.02217

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 Rescooped by Complexity Digest from Social Foraging

Mapping Systemic Risk: Critical Degree and Failures Distribution in Financial Networks

The financial crisis illustrated the need for a functional understanding of systemic risk in strongly interconnected financial structures. Dynamic processes on complex networks being intrinsically difficult to model analytically, most recent studies of this problem have relied on numerical simulations. Here we report analytical results in a network model of interbank lending based on directly relevant financial parameters, such as interest rates and leverage ratios. We obtain a closed-form formula for the “critical degree” (the number of creditors per bank below which an individual shock can propagate throughout the network), and relate failures distributions to network topologies, in particular scalefree ones. Our criterion for the onset of contagion turns out to be isomorphic to the condition for cooperation to evolve on graphs and social networks, as recently formulated in evolutionary game theory. This remarkable connection supports recent calls for a methodological rapprochement between finance and ecology.

Smerlak M, Stoll B, Gupta A, Magdanz JS (2015) Mapping Systemic Risk: Critical Degree and Failures Distribution in Financial Networks. PLoS ONE 10(7): e0130948. http://dx.doi.org/10.1371/journal.pone.0130948 ;

Via Ashish Umre
malek's comment, August 13, 10:23 AM
though provoking, wonder why we coin financial risk with moral hazards?
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Forest health in a changing world

Recognizing the signs of ill forest health and teasing apart the causes are important both for sustaining the services that humans rely on and for the effective conservation of forest biomes. Understanding how we influence forest health and function is a key challenge for the future, as we increasingly realize the importance of forests to the maintenance of a healthy planet.

Forest health in a changing world
Andrew Sugden, Julia Fahrenkamp-Uppenbrink, David Malakoff, Sacha Vignieri

Science 21 August 2015:
Vol. 349 no. 6250 pp. 800-801
http://dx.doi.org/10.1126/science.349.6250.800 ;

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Plant microbiome blueprints

Just as the number of petals in a flower or the number of limbs on an animal follow predictable rules, host-associated microbial communities (“microbiomes”) have predictable compositions. At the level of bacterial phylum, the structure of the host-associated microbiome is conserved across individuals of a species (1, 2). The consistency and predictability of host-associated microbiomes—like many of the phenotypes of a particular multicellular organism—suggest that they too may, in part, be under the regulation of a genetic blueprint. Indeed, evidence in animals shows that through production of broad-spectrum antimicrobials, the innate immune system shapes the composition of the gut microbiome (3, 4). On page 860 of this issue, Lebeis et al. (5) reveal a critical role of the plant hormone salicylic acid in determining the higher-order organization of the root-associated microbiome of the reference plant Arabidopsis thaliana.

Plant microbiome blueprints
Cara H. Haney, Frederick M. Ausubel

Science 21 August 2015:
Vol. 349 no. 6250 pp. 788-789

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 Suggested by Juan I. Perotti

Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the {\it hierarchical mutual information}, which is a generalization of the traditional mutual information, and allows to compare hierarchical partitions and hierarchical community structures. The {\it normalized} version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here, the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies, and on the hierarchical community structure of artificial and empirical networks. Furthermore, the experiments illustrate some of the practical applications of the hierarchical mutual information. Namely, the comparison of different community detection methods, and the study of the the consistency, robustness and temporal evolution of the hierarchical modular structure of networks.

Hierarchical mutual information for the comparison of hierarchical community structures in complex networks
Juan Ignacio Perotti, Claudio Juan Tessone, Guido Caldarelli

http://arxiv.org/abs/1508.04388

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Artificial Life Art, Creativity, and Techno-hybridization (editor's introduction)

Artists and engineers have devised lifelike technology for millennia. Their ingenious devices have often prompted inquiry into our preferences, prejudices, and beliefs about living systems, especially regarding their origins, status, constitution, and behavior. A recurring fabrication technique is shared across artificial life art, science, and engineering. This involves aggregating representations or re-creations of familiar biological parts—techno-hybridization—but the motives of practitioners may differ markedly. This article, and the special issue it introduces, explores how ground familiar to contemporary artificial life science and engineering has been assessed and interpreted in parallel by (a) artists and (b) theorists studying creativity explicitly. This activity offers thoughtful, alternative perspectives on artificial life science and engineering, highlighting and sometimes undermining the fields' underlying assumptions, or exposing avenues that are yet to be explored outside of art. Additionally, art has the potential to engage the general public, supporting and exploring the findings of scientific research and engineering. This adds considerably to the maturity of a culture tackling the issues the discipline of artificial life raises.

Artificial Life Art, Creativity, and Techno-hybridization (editor's introduction)
Alan Dorin

Artificial Life

Summer 2015, Vol. 21, No. 3, Pages 261-270
http://dx.doi.org/10.1162/ARTL_e_00166 ;

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Creativity and ALife

Three forms of creativity are exemplified in biology and studied in ALife. Combinational creativity exists as the first step in genetic algorithms. Exploratory creativity is seen in models using cellular automata or evolutionary programs. Transformational creativity can result from evolutionary programming. Even radically novel forms can do so, given input from outside the program itself. Transformational creativity appears also in reaction-diffusion models of morphogenesis. That there are limits to biological creativity is suggested by ALife work bearing on instances of biological impossibility.

Creativity and ALife
Margaret A. Boden

Artificial Life

Summer 2015, Vol. 21, No. 3, Pages 354-365
http://dx.doi.org/10.1162/ARTL_a_00176&nbsp;

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Engineering an Anthropocene Citizenship Framework

This article presents an Anthropocene citizen-cantered framework by incorporating the neuroscience of sustainability related stressors, the biology of collaboration in multi-agent ecosystems such as urban systems, and by emphasising on the importance of harnessing the collective intelligence of the crowd in addressing wicked challenges of sustainable development. The Anthropocene citizenship framework aims to transcend the cognitive model of global citizenship and sustainability to a dynamic, resilient and thriving mental model of collective cooperation.

Engineering an Anthropocene Citizenship Framework
Shima Beigi

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 Suggested by eflegara

The joy of transient chaos

We intend to show that transient chaos is a very appealing, but still not widely appreciated, subfield of nonlinear dynamics. Besides flashing its basic properties and giving a brief overview of the many applications, a few recent transient-chaos-related subjects are introduced in some detail. These include the dynamics of decision making, dispersion, and sedimentation of volcanic ash, doubly transient chaos of undriven autonomous mechanical systems, and a dynamical systems approach to energy absorption or explosion.

The joy of transient chaos

Tamás Tél

Chaos 25, 097619 (2015)

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Graphene kirigami

The ratio of in-plane stiffness to out-of-plane bending stiffness of graphene is shown to be similar to that of a piece of paper, which allows ideas from kirigami (a variation of origami that allows cutting) to be applied to micrometre-scale graphene sheets to build mechanically stretchable yet robust electrodes, springs and hinges.

Graphene kirigami
• Melina K. Blees, Arthur W. Barnard, Peter A. Rose, Samantha P. Roberts, Kathryn L. McGill, Pinshane Y. Huang, Alexander R. Ruyack, Joshua W. Kevek, Bryce Kobrin, David A. Muller & Paul L. McEuen

Nature 524, 204–207 (13 August 2015) http://dx.doi.org/10.1038/nature14588 ;

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 Suggested by Joseph Lizier

Non-parametric estimation of Fisher information from real data

The Fisher Information matrix is a widely used measure for applications ranging from statistical inference, information geometry, experiment design, to the study of criticality in biological systems. Yet there is no commonly accepted non-parametric algorithm to estimate it from real data. In this rapid communication we show how to accurately estimate the Fisher information in a nonparametric way. We also develop a numerical procedure to minimize the errors by choosing the interval of the finite difference scheme necessary to compute the derivatives in the definition of the Fisher information. Our method uses the recently published "Density Estimation using Field Theory" algorithm to compute the probability density functions for continuous densities. We use the Fisher information of the normal distribution to validate our method and as an example we compute the temperature component of the Fisher Information Matrix in the two dimensional Ising model and show that it obeys the expected relation to the heat capacity and therefore peaks at the phase transition at the correct critical temperature.

"Non-parametric estimation of Fisher information from real data"
Omri Har Shemesh, Rick Quax, Borja Miñano, Alfons G. Hoekstra, Peter M. A. Sloot
arXiv:1507.00964 [stat.CO], 2014
http://arxiv.org/abs/1507.00964

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We investigate the time evolution of lead changes within individual games of competitive team sports. Exploiting ideas from the theory of random walks, the number of lead changes within a single game follows a Gaussian distribution. We show that the probability that the last lead change and the time of the largest lead size are governed by the same arcsine law, a bimodal distribution that diverges at the start and at the end of the game. We also determine the probability that a given lead is “safe” as a function of its size L and game time t. Our predictions generally agree with comprehensive data on more than 1.25 million scoring events in roughly 40 000 games across four professional or semiprofessional team sports, and are more accurate than popular heuristics currently used in sports analytics.

A. Clauset, M. Kogan, and S. Redner
Phys. Rev. E 91, 062815

http://dx.doi.org/10.1103/PhysRevE.91.062815
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Automata networks model for alignment and least effort on vocabulary formation

Can artificial communities of agents develop language with scaling relations close to the Zipf law? As a preliminary answer to this question, we propose an Automata Networks model of the formation of a vocabulary on a population of individuals, under two in principle opposite strategies: the alignment and the least effort principle. Within the previous account to the emergence of linguistic conventions (specially, the Naming Game), we focus on modeling speaker and hearer efforts as actions over their vocabularies and we study the impact of these actions on the formation of a shared language. The numerical simulations are essentially based on an energy function, that measures the amount of local agreement between the vocabularies. The results suggests that on one dimensional lattices the best strategy to the formation of shared languages is the one that minimizes the efforts of speakers on communicative tasks.

Automata networks model for alignment and least effort on vocabulary formation
Javier Vera, Felipe Urbina, Eric Goles

http://arxiv.org/abs/1508.01577

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 Suggested by eflegara