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SOCNET  January 2014

SOCNET January 2014

Subject:

some complexity digest posts - with thanks

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Mon, 13 Jan 2014 09:49:34 -0500

Content-Type:

MULTIPART/MIXED

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TEXT/PLAIN (182 lines)

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


   Barry Wellman
  _______________________________________________________________________

   NetLab                        FRSC                      INSNA Founder
   Faculty of Information (iSchool)                 611 Bissell Building
   140 St. George St.    University of Toronto    Toronto Canada M5S 3G6
   http://www.chass.utoronto.ca/~wellman          twitter: @barrywellman
                  NSA/CSEC: Canadian and American citizen
   NETWORKED:The New Social Operating System. Lee Rainie & Barry Wellman
   MIT Press            http://amzn.to/zXZg39      Print $20  Kindle $16
                  Old/NewCyberTimes http://bit.ly/c8N9V8
   ________________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 13 Jan 2014 07:12:31 -0600
From: Complexity Digest Administration <[log in to unmask]>
To: [log in to unmask]
Subject: [comdig] Latest Complexity Digest Posts


Rebalancing the Global Economy

    This special report includes 23 articles on topics ranging from technology, innovation and brand building to infrastructure, entrepreneurship and social impact. Current trends and recent developments shaping today  s global marketplace are covered, as are specific companies, industries and countries.
http://knowledge.wharton.upenn.edu/special-report/rebalancing-global-economy/

See it on Scoop.it (http://www.scoop.it/t/papers/p/4013972281/2014/01/10/rebalancing-the-global-economy) , via Papers (http://www.scoop.it/t/papers)

Scale-free power-laws as interaction between progress and diffusion

    While scale-free power-laws are frequently found in social and technological systems, their authenticity, origin, and gained insights are often questioned, and rightfully so. The article presents a newly found rank-frequency power-law that aligns the top-500 supercomputers according to their performance. Pursuing a cautious approach in a systematic way, we check for authenticity, evaluate several potential generative mechanisms, and ask the   so what   question. We evaluate and finally reject the applicability of well-known potential generative mechanisms such as preferential attachment, self-organized criticality, optimization, and random observation. Instead, the microdata suggest that an inverse relationship between exponential technological progress and exponential technology diffusion through social networks results in the identified fat-tail distribution. This newly identified generative mechanism suggests that the supply and demand of technology (  technology push   and
  demand pull  ) align in exponential synchronicity, providing predictive insights into the evolution of highly uncertain technology markets.

Scale-free power-laws as interaction between progress and diffusion
Martin Hilbert

Complexity
Early View

http://dx.doi.org/10.1002/cplx.21485

See it on Scoop.it (http://www.scoop.it/t/papers/p/4013971251/2014/01/10/scale-free-power-laws-as-interaction-between-progress-and-diffusion) , via Papers (http://www.scoop.it/t/papers)


Structural balance in the social networks of a wild mammal

      We tested the theory of structural balance (i.e.   the enemy of my enemy is my friend  ) in rock hyrax social networks.
  We found that in accordance with structural balance, hyraxes tend to form balanced triads.
  Hyraxes changed their social configurations over time, moving into more balanced triads.
  New individuals in the population introduced social instability.
  Triad sex ratio affected the triad type it changed to.

Structural balance in the social networks of a wild mammal
Amiyaal Ilany, Adi Barocas, Lee Koren, Michael Kam, Eli Geffen

Animal Behaviour
Volume 85, Issue 6, June 2013, Pages 1397  1405

http://dx.doi.org/10.1016/j.anbehav.2013.03.032

See it on Scoop.it (http://www.scoop.it/t/papers/p/4013969870/2014/01/10/structural-balance-in-the-social-networks-of-a-wild-mammal) , via Papers (http://www.scoop.it/t/papers)


Human opinion dynamics: An inspiration to solve complex optimization problems

    Human interactions give rise to the formation of different kinds of 
opinions in a society. The study of formations and dynamics of opinions 
has been one of the most important areas in social physics. The opinion 
dynamics and associated social structure leads to decision making or so 
called opinion consensus. Opinion formation is a process of collective 
intelligence evolving from the integrative tendencies of social influence 
with the disintegrative effects of individualisation, and therefore could 
be exploited for developing search strategies. Here, we demonstrate that 
human opinion dynamics can be utilised to solve complex mathematical 
optimization problems. The results have been compared with a standard 
algorithm inspired from bird flocking behaviour and the comparison proves 
the efficacy of the proposed approach in general. Our investigation may 
open new avenues towards understanding the collective decision making.

Human opinion dynamics: An inspiration to solve complex optimization problems
Rishemjit Kaur, Ritesh Kumar, Amol P. Bhondekar & Pawan Kapur

Scientific Reports 3, Article number: 3008 http://dx.doi.org/10.1038/srep03008

See it on Scoop.it (http://www.scoop.it/t/papers/p/4013965753/2014/01/10/human-opinion-dynamics-an-inspiration-to-solve-complex-optimization-problems) , via Papers (http://www.scoop.it/t/papers)


A Scalable Heuristic for Viral Marketing Under the Tipping Model

    In a "tipping" model, each node in a social network, representing an individual, adopts a property or behavior if a certain number of his incoming neighbors currently exhibit the same. In viral marketing, a key problem is to select an initial "seed" set from the network such that the entire network adopts any behavior given to the seed. Here we introduce a method for quickly finding seed sets that scales to very large networks. Our approach finds a set of nodes that guarantees spreading to the entire network under the tipping model. After experimentally evaluating 31 real-world networks, we found that our approach often finds seed sets that are several orders of magnitude smaller than the population size and outperform nodal centrality measures in most cases. In addition, our approach scales well - on a Friendster social network consisting of 5.6 million nodes and 28 million edges we found a seed set in under 3.6 hours. Our experiments also indicate that our algorithm
provides small seed sets even if high-degree nodes are removed. Lastly, we find that highly clustered local neighborhoods, together with dense network-wide community structures, suppress a trend's ability to spread under the tipping model.

A Scalable Heuristic for Viral Marketing Under the Tipping Model
Paulo Shakarian, Sean Eyre, Damon Paulo

http://arxiv.org/abs/1309.2963

See it on Scoop.it (http://www.scoop.it/t/papers/p/4013964777/2014/01/10/a-scalable-heuristic-for-viral-marketing-under-the-tipping-model) , via Papers (http://www.scoop.it/t/papers)


Epidemics on social networks

    Since its first formulations almost a century ago, mathematical models for disease spreading contributed to understand, evaluate and control the epidemic processes.They promoted a dramatic change in how epidemiologists thought of the propagation of infectious diseases.In the last decade, when the traditional epidemiological models seemed to be exhausted, new types of models were developed.These new models incorporated concepts from graph theory to describe and model the underlying social structure.Many of these works merely produced a more detailed extension of the previous results, but some others triggered a completely new paradigm in the mathematical study of epidemic processes. In this review, we will introduce the basic concepts of epidemiology, epidemic modeling and networks, to finally provide a brief description of the most relevant results in the field.

Epidemics on social networks
Marcelo N. Kuperman

http://arxiv.org/abs/1312.3838

See it on Scoop.it (http://www.scoop.it/t/papers/p/4013855825/2014/01/08/epidemics-on-social-networks) , via Papers (http://www.scoop.it/t/papers)



Persistence of social signatures in human communication

    We combine cell phone data with survey responses to show that a 
person  s social signature, as we call the pattern of their interactions 
with different friends and family members, is remarkably robust. People 
focus a high proportion of their communication efforts on a small number 
of individuals, and this behavior persists even when there are changes in 
the identity of the individuals involved. Although social signatures vary 
between individuals, a given individual appears to retain a specific 
social signature over time. Our results are likely to reflect limitations 
in the ability of humans to maintain many emotionally close relationships, 
both because of limited time and because the emotional   capital   that 
individuals can allocate between family members and friends is finite.

Persistence of social signatures in human communication

Jari Saramńki, E. A. Leicht, Eduardo Lˇpez, Sam G. B. Roberts, Felix Reed-Tsochas, and Robin I. M. Dunbar

PNAS

http://dx.doi.org/10.1073/pnas.1308540110

See it on Scoop.it (http://www.scoop.it/t/papers/p/4013799205/2014/01/07/persistence-of-social-signatures-in-human-communication) , via Papers (http://www.scoop.it/t/papers)



Guided Self-Organization: Inception (Emergence, Complexity and Computation): Mikhail Prokopenko

    Is it possible to guide the process of self-organisation towards specific patterns and outcomes? Wouldn  t this be self-contradictory? After all, a self-organising process assumes a transition into a more organised form, or towards a more structured functionality, in the absence of centralised control. Then how can we place the guiding elements so that they do not override rich choices potentially discoverable by an uncontrolled process?
This book presents different approaches to resolving this paradox. In doing so, the presented studies address a broad range of phenomena, ranging from autopoietic systems to morphological computation, and from small-world networks to information cascades in swarms. A large variety of methods is employed, from spontaneous symmetry breaking to information dynamics to evolutionary algorithms, creating a rich spectrum reflecting this emerging field.
Demonstrating several foundational theories and frameworks, as well as innovative practical implementations, Guided Self-Organisation: Inception, will be an invaluable tool for advanced students and researchers in a multiplicity of fields across computer science, physics and biology, including information theory, robotics, dynamical systems, graph theory, artificial life, multi-agent systems, theory of computation and machine learning.

See it on Scoop.it (http://www.scoop.it/t/cxbooks/p/4013716843/2014/01/06/guided-self-organization-inception-emergence-complexity-and-computation-mikhail-prokopenko) , via CxBooks (http://www.scoop.it/t/cxbooks)


Consensus and Synchronization in Complex Networks (by Ljupco Kocarev)

    In this book for the first time two scientific fields - consensus formation and synchronization of communications - are presented together and examined through their interrelational aspects, of rapidly growing importance. Both fields have indeed attracted enormous research interest especially in relation to complex networks.

In networks of dynamic systems (or agents), consensus means to reach an agreement regarding a certain quantity of interest that depends on the state of all dynamical systems (agents). Consensus problems have a long history in control theory and computer sciences, and form the foundation of the field of distributed computing. Synchronization, which defines correlated-in-time behavior between different processes and roots going back to Huygens to the least, is now a highly popular, exciting and rapidly developing topic, with applications ranging from biological networks to mathematical epidemiology, and from processing information in the brain to engineering of communications devices.

The book reviews recent finding in both fields and describes novel approaches to consensus formation, where consensus is realized as an instance of the nonlinear dynamics paradigm of chaos synchronization. The chapters are written by world-known experts in both fields and cover topics ranging from fundaments to various applications of consensus and synchronization.



See it on Scoop.it (http://www.scoop.it/t/cxbooks/p/4012405979/2014/01/06/consensus-and-synchronization-in-complex-networks-by-ljupco-kocarev) , via CxBooks (http://www.scoop.it/t/cxbooks)


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