***** To join INSNA, visit http://www.insna.org *****
Barry Wellman
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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
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Quantitative Analysis of Culture Using Millions of Digitized Books ,
Science
Abstract: We constructed a corpus of digitized texts containing about 4%
of all books ever printed. Analysis of this corpus enables us to
investigate cultural trends quantitatively. We survey the vast terrain of
culturomics, focusing on linguistic and cultural phenomena that were
reflected in the English language between 1800 and 2000. We show how this
approach can provide insights about fields as diverse as lexicography, the
evolution of grammar, collective memory, the adoption of technology, the
pursuit of fame, censorship, and historical epidemiology. Culturomics
extends the boundaries of rigorous quantitative inquiry to a wide array of
new phenomena spanning the social sciences and the humanities.
* [3] Quantitative Analysis of Culture Using Millions of Digitized Books,
Jean-Baptiste Michel, Yuan Kui Shen, Aviva Presser Aiden, Adrian Veres,
Matthew K. Gray, The Google Books Team, Joseph P. Pickett, Dale Hoiberg,
Dan Clancy, Peter Norvig, Jon Orwant, Steven Pinker, Martin A. Nowak, and
Erez Lieberman Aiden, 2011/01/14, DOI: 10.1126/science.1199644, Science
Vol. 331 no. 6014 pp. 176-182
[3] http://dx.doi.org/10.1126/science.1199644
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Communication: Mapping science , Nature
Excerpt: [11]
In the Atlas of Science, information scientist Katy Börner highlights
examples that summarize the evolution of research and its interlocking
communities in pictorial form. The book accompanies Börner's ambitious
travelling exhibitions, Places & Spaces: Mapping Science, an ongoing
programme of well-crafted visual presentations that have conveyed aspects
of science to the public in libraries and museums since 2005 ( [12]
http://scimaps.org).
* [13] Communication: Mapping science, Ben Shneiderman, 2010/12/22, DOI:
10.1038/4681037a, Nature 468, 1037
[11]
http://www.amazon.com/gp/product/0262014459?ie=UTF8&tag=complexes-20&linkCode=as2&c
amp=1789&creative=9325&creativeASIN=0262014459
[12] http://scimaps.org
[13] http://dx.doi.org/10.1038/4681037a
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Continuous-time model of structural balance , PNAS
Excerpt: It is not uncommon for certain social networks to divide into two
opposing camps in response to stress. This happens, for example, in
networks of political parties during winner-takes-all elections, in
networks of companies competing to establish technical standards, and in
networks of nations faced with mounting threats of war. A simple model for
these two-sided separations is the dynamical system dX/dt = X^2, where X
is a matrix of the friendliness or unfriendliness between pairs of nodes
in the network. (...)
* [14] Continuous-time model of structural balance, Seth A. Marvel, Jon
Kleinberg, Robert D. Kleinberg, Steven H. Strogatz, 2011/01/03, DOI:
10.1073/pnas.1013213108, PNAS, Published online before print
[14] http://dx.doi.org/10.1073/pnas.1013213108
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08. Modular Random Boolean Networks , arXiv
Abstract: Random Boolean networks (RBNs) have been a popular model of
genetic regulatory networks for more than four decades. However, most RBN
studies have been made with regular topologies, while real regulatory
networks have been found to be modular. In this work, we extend classical
RBNs to define modular RBNs. Statistical experiments and analytical
results show that modularity has a strong effect on the properties of
RBNs. In particular, modular RBNs are closer to criticality than regular
RBNs.
* [15] Modular Random Boolean Networks, Rodrigo Poblanno-Balp and Carlos
Gershenson, 2011/01/10, arXiv:1101.1893
[15] http://arXiv.org/abs/1101.1893
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An Agent-Based Approach to Self-Organized Production , arXiv
Excerpt: The chapter describes the modeling of a material handling system
with the production of individual units in a scheduled order. The units
represent the agents in the model and are transported in the system which
is abstracted as a directed graph. Since the hindrances of units on their
path to the destination can lead to inefficiencies in the production, the
blockages of units are to be reduced. Therefore, the units operate in the
system by means of local interactions in the conveying elements and
indirect interactions based on a measure of possible hindrances. If most
of the units behave cooperatively ("socially"), the blockings in the
system are reduced.
* [16] An Agent-Based Approach to Self-Organized Production, Thomas
Seidel, Jeanette Hartwig, Richard L. Sanders, and Dirk Helbing,
2010/12/21, arXiv:1012.4645
[16] http://arXiv.org/abs/1012.4645
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The AHA! Experience: Creativity Through Emergent Binding in Neural
Networks
, Cognitive Science
Abstract: Many kinds of creativity result from combination of mental
representations. This paper provides a computational account of how
creative thinking can arise from combining neural patterns into ones that
are potentially novel and useful. We defend the hypothesis that such
combinations arise from mechanisms that bind together neural activity by a
process of convolution, a mathematical operation that interweaves
structures. We describe computer simulations that show the feasibility of
using convolution to produce emergent patterns of neural activity that can
support cognitive and emotional processes underlying human creativity.
* [17] The AHA! Experience: Creativity Through Emergent Binding in Neural
Networks, Paul Thagard, Terrence C. Stewart, 2011/01-02, DOI:
10.1111/j.1551-6709.2010.01142.x, Cognitive Science Volume 35, Issue 1,
pages
133, January/February 2011
[17] http://dx.doi.org/10.1111/j.1551-6709.2010.01142.x
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Selection in Scientific Networks , arXiv
Excerpt: One of the most interesting scientific challenges nowadays deals
with the analysis and the understanding of complex networks' dynamics. A
major issue is the definition of new frameworks for the exploration of the
dynamics at play in real dynamic networks. Here, we focus on scientific
communities by analyzing the "social part" of Science through a
descriptive approach that aims at identifying the social determinants
(e.g. goals and potential interactions among individuals) behind the
emergence and the resilience of scientific communities.
* [21] Selection in Scientific Networks, Walter Quattrociocchi, Frederic
Amblard, 2010/12/20, arXiv:1012.4396
[21] http://arXiv.org/abs/1012.4396
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Percolate or die: Multi-percolation decides the struggle between
competing innovations , arXiv
Abstract: Competition is one of the most fundamental phenomena in physics,
biology and economics. Recent studies of the competition between
innovations have highlighted the influence of switching costs and
interaction networks, but the problem is still puzzling. We introduce a
model that reveals a novel multi-percolation process, which governs the
struggle of innovations trying to penetrate a market. We find that
innovations thrive as long as they percolate in a population, and one
becomes dominant when it is the only one that percolates. Besides offering
a theoretical framework to understand the diffusion of competing
innovations in social networks, our results are also relevant to model
other problems such as opinion formation, political polarization, survival
of languages and the spread of health behavior.
* [22] Percolate or die: Multi-percolation decides the struggle between
competing innovations, Carlos P. Roca, Moez Draief and Dirk Helbing,
2011/01/04, arXiv:1101.0775
[22] http://arXiv.org/abs/1101.0775
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Self-Organizing Systems , Springer
About: This book constitutes the refereed proceedings of the 5th
International Workshop on Self-Organizing Systems, IWSOS 2011, held in
Karlsruhe, Germany, in February 2011. The 9 revised full papers presented
together with 1 invited paper were carefully selected from 25 initial
submissions. It was the 5th workshop in a series of multidisciplinary
events dedicated to self-organization in networked systems with main focus
on communication and computer networks. The papers address theoretical
aspects of self-organization as well as applications in communication and
computer networks and robot networks.
* [27] Self-Organizing Systems, Bettstetter, Christian; Gershenson, Carlos
(Eds.), 2011/03/08, DOI: 978-3-642-19166-4, Springer
http://www.springer.com/computer/communication+networks/book/978-3-642-19166-4
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