Print

Print


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

Busy calypsoing to the Mighty Shadow, so didn't get to this till now.

This set is especially full and stimulating
 Barry Wellman
 _______________________________________________________________________

  S.D. Clark Professor of Sociology, FRSC               NetLab Director
  Department of Sociology                  725 Spadina Avenue, Room 388
  University of Toronto   Toronto Canada M5S 2J4   twitter:barrywellman
  http://www.chass.utoronto.ca/~wellman             fax:+1-416-978-3963
  Updating history:      http://chass.utoronto.ca/oldnew/cybertimes.php
 _______________________________________________________________________

Revisiting the Foundations of Network Analysis , Science

Abstract: Network analysis has emerged as a powerful way of studying
phenomena as diverse as interpersonal interaction, connections among
neurons, and the structure of the Internet. Appropriate use of network
analysis depends, however, on choosing the right network representation
for the problem at hand.

* [3] Revisiting the Foundations of Network Analysis, Carter T. Butts,
2009/07/24, DOI: 10.1126/science.1171022, Science Vol. 325. no. 5939, pp.
414 - 416

[3] http://dx.doi.org/10.1126/science.1171022

---------------

 Economic Networks: The New Challenges , Science

Abstract: The current economic crisis illustrates a critical need for new
and fundamental understanding of the structure and dynamics of economic
networks. Economic systems are increasingly built on interdependencies,
implemented through trans-national credit and investment networks, trade
relations, or supply chains that have proven difficult to predict and
control. We need, therefore, an approach that stresses the systemic
complexity of economic networks and that can be used to revise and extend
established paradigms in economic theory. This will facilitate the design
of policies that reduce conflicts between individual interests and global
efficiency, as well as reduce the risk of global failure by making
economic networks more robust.

* [6] Economic Networks: The New Challenges, Frank Schweitzer, Giorgio
Fagiolo, Didier Sornette, Fernando Vega-Redondo, Alessandro Vespignani,
Douglas R. White, 2009/07/24, DOI: 10.1126/science.1173644, Science Vol.
325. no. 5939, pp. 422 - 425

[6] http://www.sciencemag.org/cgi/content/full/325/5939/422

----------------------

 Predicting the Behavior of Techno-Social Systems , Science

Abstract: We live in an increasingly interconnected world of techno-
social systems, in which infrastructures composed of different
technological layers are interoperating within the social component that
drives their use and development. Examples are provided by the Internet,
the World Wide Web, WiFi communication technologies, and transportation
and mobility infrastructures. The multiscale nature and complexity of
these networks are crucial features in understanding and managing the
networks. The accessibility of new data and the advances in the theory and
modeling of complex networks are providing an integrated framework that
brings us closer to achieving true predictive power of the behavior of
techno-social systems.

* [7] Predicting the Behavior of Techno-Social Systems, Alessandro
Vespignani, 2009/07/24, DOI: 10.1126/science.1171990, Science Vol. 325.
no. 5939, pp. 425 -428

[7] http://dx.doi.org/10.1126/science.1171990

-------------------

ctious
diseases , arXiv

Excerpt: Among the realistic ingredients to be considered in the
computational modeling of infectious diseases, human mobility represents a
crucial challenge both on the theoretical side and in view of the limited
availability of empirical data. In order to study the interplay between
small-scale commuting flows and long-range airline traffic in shaping the
spatio-temporal pattern of a global epidemic we i) analyze mobility data
from 29 countries around the world and find a gravity model able to
provide a global description of commuting patterns up to 300 kms; ii)
integrate in a worldwide structured metapopulation epidemic model a
time-scale separation technique for evaluating the force of infection due
to multiscale mobility processes in the disease dynamics.

* [10] Multiscale mobility networks and the large scale spreading of
infectious diseases, Duygu Balcan, Vittoria Colizza, Bruno Goncalves, Hao
Hu, Jose J. Ramasco, and Alessandro Vespignani, 2009/07/20,
arXiv:0907.3304 [10] http://arXiv.org/abs/0907.3304

--------------------------------

Common group dynamic drives modern epidemics across social, financial and
biological domains , arXiv

Excerpt: We show that qualitatively different epidemic-like processes from
distinct societal domains (finance, social and commercial blockbusters,
epidemiology) can be quantitatively understood using the same unifying
conceptual framework taking into account the interplay between the
timescales of the grouping and fragmentation of social groups together
with typical epidemic transmission processes. (...) Our results reveal a
new minimally- invasive dynamical method for controlling such outbreaks,
help fill a gap in existing epidemiological theory, and offer a new
understanding of complex system response functions.

* [11] Common group dynamic drives modern epidemics across social,
financial and biological domains, Zhenyuan Zhao, Juan Pablo Calder\'on,
Chen Xu, Dan Fenn, Didier Sornette, Riley Crane, Pak Ming Hui, Neil F.
Johnson, 2009/07/21, arXiv:0907.3600
[11] http://arXiv.org/abs/0907.3600

--------------------------

Identification of a Topological Characteristic Responsible for the
Biological Robustness of Regulatory Networks , PLoS Comput Biol

Excerpt: Attribution of biological robustness to the specific structural
properties of a regulatory network is an important yet unsolved problem in
systems biology. It is widely believed that the topological
characteristics of a biological control network largely determine its
dynamic behavior, yet the actual mechanism is still poorly understood.
Here, we define a novel structural feature of biological networks, termed
Ô^└^ěregulation entropyÔ^└^┘, to quantitatively assess the influence of
network topology on the robustness of the systems.

* [26] Identification of a Topological Characteristic Responsible for the
Biological Robustness of Regulatory Networks, Wu Y, Zhang X, Yu J, Ouyang
Q, 2009/07/24, DOI: 10.1371/journal.pcbi.1000442, PLoS Comput Biol 5(7):
e1000442 [26] http://dx.doi.org/10.1371/journal.pcbi.1000442

-------------------

 Emergent Network Structure, Evolvable Robustness, And Nonlinear Effects
Of Point Mutations In An Artificial Genome Model , Advances in Complex
Systems (ACS)

Abstract: Genetic regulation is a key component in development, but a
clear understanding of the structure and dynamics of genetic networks is
not yet at hand. We investigate these properties within an artificial
genome model. We analyze statistical properties of randomly generated
genomes both on the sequence and network level, and show that this model
correctly predicts the frequency of genes in genomes as found in
experimental data. Using an evolutionary algorithm based on stabilizing
selection for a phenotype, we show that dynamical robustness against
single base-mutations, as against random changes in initial states of
regulatory dynamics, can emerge in parallel.

* [27] Emergent Network Structure, Evolvable Robustness, And Nonlinear
Effects Of Point Mutations In An Artificial Genome Model, Thimo Rohlf,
Christopher R. Winkler, 2009/07/15, DOI: 10.1142/S0219525909002210,
Advances in Complex Systems (ACS), vol. 12, issue 03, pages 293-310 *
Contributed by [28] Anton Joha
[27] http://dx.doi.org/10.1142/S0219525909002210

--------------------

Dragon-Kings, Black Swans and the Prediction of Crises , Arxiv

Abstract: We develop the concept of ``dragon-kings'' corresponding to
meaningful outliers, which are found to coexist with power laws in the
distributions of event sizes under a broad range of conditions in a large
variety of systems. These dragon-kings reveal the existence of mechanisms
of self-organization that are not apparent otherwise from the distribution
of their smaller siblings. We present a generic phase diagram to explain
the generation of dragon-kings and document their presence in six
different examples.  (...)

* [45] Dragon-Kings, Black Swans and the Prediction of Crises, Didier
Sornette, 2009/07/24, DOI: arXiv:0907.4290v1, Arxiv (arXiv:0907.4290v1)
[45] http://arxiv.org/abs/0907.4290

_____________________________________________________________________
SOCNET is a service of INSNA, the professional association for social
network researchers (http://www.insna.org). To unsubscribe, send
an email message to [log in to unmask] containing the line
UNSUBSCRIBE SOCNET in the body of the message.