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SOCNET  September 2014

SOCNET September 2014

Subject:

selected [comdig] Latest Complexity Digest Posts (fwd)

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Mon, 22 Sep 2014 08:56:56 -0400

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TEXT/PLAIN (139 lines)

*****  To join INSNA, visit http://www.insna.org  *****




   Barry Wellman
  _______________________________________________________________________
   FRSC		              NetLab Network              INSNA Founder
                      Faculty of Information (iSchool)
   University of Toronto                          Toronto Canada M5S 3G6
   http://www.chass.utoronto.ca/~wellman          twitter: @barrywellman
   NETWORKED:The New Social Operating System. Lee Rainie & Barry Wellman
   MIT Press            http://amzn.to/zXZg39      Print $15  Kindle $9
                  Old/NewCyberTimes http://bit.ly/c8N9V8
   ________________________________________________________________________



Introduction: Simulation, Visualization, and Scientific Understanding

    The papers in this special issue (http://www.mitpressjournals.org/toc/posc/22/3) address questions at the intersection of these three topics: scientific understanding, computer simulation and vi- sualization. They are a subset of the papers presented at a workshop on scientific understanding held at the Lorentz Center (Leiden, The Nether- lands) in 2010. Spanning a wide range of scientific fields  from sociology to biology to climate science to fundamental physics  as a group they both reveal common threads and serve as a reminder of the diversity of practices in science, including thought experiments, theoretical analysis on paper, computer simulations, and data-intensive research employing online databases.

Introduction: Simulation, Visualization, and Scientific Understanding
Henk W. de Regt, Wendy S. Parker

Fall 2014, Vol. 22, No. 3, Pages 311-317
http://dx.doi.org/10.1162/POSC_e_00135

See it on Scoop.it (http://www.scoop.it/t/cxannouncements/p/4028342096/2014/09/20/introduction-simulation-visualization-and-scientific-understanding) , via CxAnnouncements (http://www.scoop.it/t/cxannouncements)


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

See it on Scoop.it (http://www.scoop.it/t/papers/p/4028281179/2014/09/18/identification-of-core-periphery-structure-in-networks) , via Papers (http://www.scoop.it/t/papers)



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 Catalßn, Susanna Manrubia, JosÚ A. Cuesta

http://arxiv.org/abs/1409.4904

See it on Scoop.it (http://www.scoop.it/t/papers/p/4028282121/2014/09/18/toylife-a-computational-framework-to-study-the-multi-level-organization-of-the-genotype-phenotype-map) , via Papers (http://www.scoop.it/t/papers)


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

See it on Scoop.it (http://www.scoop.it/t/papers/p/4028273179/2014/09/18/random-walks-with-preferential-relocations-to-places-visited-in-the-past-and-their-application-to-biology) , via Papers (http://www.scoop.it/t/papers)



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

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

See it on Scoop.it (http://www.scoop.it/t/papers/p/4028272394/2014/09/18/fractional-dynamics-on-networks-emergence-of-anomalous-diffusion-and-levy-flights) , via Papers (http://www.scoop.it/t/papers)



Complex Systems Digital Campus @ ECCS'14 Panel : Science, Policy and Applications

    At ECCS'14 ( www.eccs14.eu ), Lucca, 25th Sept, 17h, panel with Mike Batty (Centre for Advanced Spatial Analysis (CASA) at University College London), and Paul Ormerod (Volterra Partners LLP, London)

See it on Scoop.it (http://www.scoop.it/t/cxconferences/p/4028189731/2014/09/17/complex-systems-digital-campus-eccs-14-panel-science-policy-and-applications) , via CxConferences (http://www.scoop.it/t/cxconferences)



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

See it on Scoop.it (http://www.scoop.it/t/papers/p/4028189703/2014/09/17/disease-induced-resource-constraints-can-trigger-explosive-pandemic) , via Papers (http://www.scoop.it/t/papers)



A Neuroscientist  s Radical Theory of How Networks Become Conscious

    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.

See it on Scoop.it (http://www.scoop.it/t/papers/p/4028065527/2014/09/15/a-neuroscientist-s-radical-theory-of-how-networks-become-conscious) , via Papers (http://www.scoop.it/t/papers)



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Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

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