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SOCNET  June 2016

SOCNET June 2016

Subject:

Latest Complexity Digest Posts(fwd)

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Mon, 6 Jun 2016 09:49:29 -0400

Content-Type:

MULTIPART/MIXED

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (214 lines)

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

Curated by

   Barry Wellman

    A vision is just a vision if it's only in your head
    Step by step, link by link, putting it together
                  Streisand/Sondheim
  _______________________________________________________________________
   NetLab Network                 FRSC                      INSNA Founder
   http://www.chass.utoronto.ca/~wellman           twitter: @barrywellman
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman
                        http://amzn.to/zXZg39
   _______________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 6 Jun 2016 11:02:59 +0000
From: "[utf-8] Complexity Digest" <[log in to unmask]>
Reply-To: [log in to unmask]
To: "[utf-8] Barry" <[log in to unmask]>
Subject: [utf-8] Latest Complexity Digest Posts

Learn about the latest and greatest related to complex systems research. More at http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=664fb86b4e&e=55e25a0e3e


Evolution of Swarming Behavior Is Shaped by How Predators Attack

    Animal grouping behaviors have been widely studied due to their 
implications for understanding social intelligence, collective cognition, 
and potential applications in engineering, artificial intelligence, and 
robotics. An important biological aspect of these studies is discerning 
which selection pressures favor the evolution of grouping behavior. In the 
past decade, researchers have begun using evolutionary computation to 
study the evolutionary effects of these selection pressures in 
predator-prey models. The selfish herd hypothesis states that concentrated 
groups arise because prey selfishly attempt to place their conspecifics 
between themselves and the predator, thus causing an endless cycle of 
movement toward the center of the group. Using an evolutionary model of a 
predator-prey system, we show that how predators attack is critical to the 
evolution of the selfish herd. Following this discovery, we show that 
density-dependent predation provides an abstraction of Hamilton's original 
formulation of domains of danger. Finally, we verify that 
density-dependent predation provides a sufficient selective advantage for 
prey to evolve the selfish herd in response to predation by coevolving 
predators. Thus, our work corroborates Hamilton's selfish herd hypothesis 
in a digital evolutionary model, refines the assumptions of the selfish 
herd hypothesis, and generalizes the domain of danger concept to 
density-dependent predation.


Evolution of Swarming Behavior Is Shaped by How Predators Attack
Randal S. Olson
David B. Knoester
Christoph Adami


Structure-based control of complex networks with nonlinear dynamics

    Given the network of interactions underlying a complex system, what can we learn about controlling such a system solely from its structure? Over a century of research in control theory has given us tools to answer this question, which were widely applied in science and engineering. Yet the current tools do not always consider the inherently nonlinear dynamics of real systems and the naturally occurring system states in their definition of "control", a term whose interpretation varies across disciplines. Here we use a new mathematical framework for structure-based control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This framework provides realizable node overrides that steer a system towards any of its natural long term dynamic behaviors and which are guaranteed to be effective regardless of the dynamic details and parameters of the underlying system. We use this
framework on several real networks, compare its predictions to those of classical control theory, and identify the topological characteristics that underlie the commonalities and differences between these frameworks. Finally, we illustrate the applicability of this new framework in the field of dynamic models by demonstrating its success in two models of a gene regulatory network and identifying the nodes whose override is necessary for control in the general case, but not in specific model instances.


Structure-based control of complex networks with nonlinear dynamics
Jorge G. T. Zańudo, Gang Yang, Réka Albert

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Applied Network Science

    The journal Applied Network Science is intended to focus on applied research benefiting from or using network science. The breadth of areas where network science is being used continues to increase and is far from reaching its peak. Annual meetings on network science continue to attract a diverse crowd˙˙from physicists to urban planners; from computer scientists to art historians. These works contribute to the body of knowledge of applications which can benefit from network science.
We have set the scope of this journal to be on ˙˙applied˙˙ work exactly to highlight the multi- and inter-disciplinary aspects of the journal. We encourage contributions from diverse fields as long as the contributions are not solely theoretical. Papers should clearly indicate how the concepts proposed can be applied to practical, real-world problems. Note that we are open to papers with theoretical results, but there should be a clear indication in the body of the work about the applied impact of the proposed theory.
Our first submissions are currently being reviewed and we expect a quick turn-around. Many other submissions are being prepared. We invite you to submit your work to demonstrate the world-wide applicability of network science.


Hocine Cherifi and Ronaldo Menezes
Editors-in-Chief

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Centrality in the Global Network of Corporate Control

    Corporations across the world are highly interconnected in a large 
global network of corporate control. This paper investigates the global 
board interlock network, covering 400,000 firms linked through 1,700,000 
edges representing shared directors between these firms. The main focus is 
on the concept of centrality, which is used to investigate the 
embeddedness of firms from a particular country within the global network. 
The study results in three contributions. First, to the best of our 
knowledge for the first time we can investigate the topology as well as 
the concept of centrality in corporate networks at a global scale, 
allowing for the largest cross-country comparison ever done in 
interlocking directorates literature. We demonstrate, amongst other 
things, extremely similar network topologies, yet large differences 
between countries when it comes to the relation between economic 
prominence indicators and firm centrality. Second, we introduce two new 
metrics that are specifically suitable for comparing the centrality 
ranking of a partition to that of the full network. Using the notion of 
centrality persistence we propose to measure the persistence of a 
partition's centrality ranking in the full network. In the board interlock 
network, it allows us to assess the extent to which the footprint of a 
national network is still present within the global network. Next, the 
measure of centrality ranking dominance tells us whether a partition 
(country) is more dominant at the top or the bottom of the centrality 
ranking of the full (global) network. Finally, comparing these two new 
measures of persistence and dominance between different countries allows 
us to classify these countries based the their embeddedness, measured 
using the relation between the centrality of a country's firms on the 
national and the global scale of the board interlock network.


Centrality in the Global Network of Corporate Control
Frank W. Takes, Eelke M. Heemskerk

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Modelling Trading Networks and the Role of Trust

    We present a simple dynamical model for describing trading interactions between agents in a social network by considering only two dynamical variables, namely money and goods or services, that are assumed conserved over the whole time span of the agents' trading transactions. A key feature of the model is that agent-to-agent transactions are governed by the price in units of money per goods, which is dynamically changing, and by a trust variable, which is related to the trading history of each agent. All agents are able to sell or buy, and the decision to do either has to do with the level of trust the buyer has in the seller, the price of the goods and the amount of money and goods at the disposal of the buyer. Here we show the results of extensive numerical calculations under various initial conditions in a random network of agents and compare the results with the available related data. In most cases the agreement between the model results and real data turns out to be
fairly good, which allow us to draw some general conclusions as how different trading strategies could affect the distribution of wealth in different kinds of societies.


Modelling Trading Networks and the Role of Trust
Rafael A. Barrio, Tzipe Govezensky, Élfego Ruiz-Gutiérrez, Kimmo K. Kaski

http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=5e1051502a&e=55e25a0e3e

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Global optimization, local adaptation and the role of growth in distribution networks

    Highly-optimized complex transport networks serve crucial functions in many man-made and natural systems such as power grids and plant or animal vasculature. Often, the relevant optimization functional is non-convex and characterized by many local extrema. In general, finding the global, or nearly global optimum is difficult. In biological systems, it is believed that natural selection slowly guides the network towards an optimized state. However, general coarse grained models for flow networks with local positive feedback rules for the vessel conductivity typically get trapped in low efficiency, local minima. In this work we show how the growth of the underlying tissue, coupled to the dynamical equations for network development, can drive the system to a dramatically improved optimal state. This general model provides a surprisingly simple explanation for the appearance of highly optimized transport networks in biology such as leaf and animal vasculature.


Global optimization, local adaptation and the role of growth in distribution networks
Henrik Ronellenfitsch, Eleni Katifori

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When face-tracking meets social networks: a story of politics in news videos

    In the age of data processing, news videos are rich mines of information. After all, the news are essentially created to convey information to the public. But can we go beyond what is directly presented to us and see a wider picture? Many works already focus on what we can discover and understand from the analysis of years of news broadcasting. These analysis bring monitoring and understanding of the activity of public figures, political strategies, explanation and even prediction of critical media events. Such tools can help public figures in managing their public image, as well as support the work of journalists, social scientists and other media experts. News analysis can also be seen from the lens of complex systems, gathering many types of entities, attributes and interactions over time. As many public figures intervene in different news stories, a first interesting task is to observe the social interactions between these actors. Towards this goal, we propose to use
video analysis to automatise the process of constructing social networks directly from news video archives. In this paper we are introducing a system deriving multiple social networks from face detections in news videos. We present preliminary results obtained from analysis of these networks, by monitoring the activity of more than a hundred public figures. We finally use these networks as a support for political studies and we provide an overview of the political landscape presented by the Japanese public broadcaster NHK over a decade of the 7 PM news archives.


When face-tracking meets social networks: a story of politics in news videos
Benjamin Renoust˙˙, Tetsuro Kobayashi, Thanh Duc Ngo, Duy-Dinh Le and Shin˙˙Ichi Satoh

Applied Network Science 2016 1:4
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Retweet networks of the European Parliament: evaluation of the community structure

    Analyzing information from social media to uncover underlying real-world phenomena is becoming widespread. The goal of this paper is to evaluate the role of Twitter in identifying communities of influence when the ˙˙ground truth˙˙ is known. We consider the European Parliament (EP) Twitter users during a period of one year, in which they posted over 560,000 tweets. We represent the influence on Twitter by the number of retweets users get. We construct two networks of influence: (i) core, where both users are the EP members, and (ii) extended, where one user can be outside the EP. We compare the detected communities in both networks to the ˙˙ground truth˙˙: the political group, country, and language of the EP members. The results show that the core network closely matches the political groups, while the extended network best reflects the country of origin. This provides empirical evidence that the formation of retweet networks and community detection are appropriate tools to reveal
real-world relationships, and can be used to uncover hidden properties when the ˙˙ground truth˙˙ is not known.


Retweet networks of the European Parliament: evaluation of the community structure
Darko Cherepnalkoski ˙˙and Igor Mozeti˙˙

Applied Network Science 2016 1:2
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Towards a standard sampling methodology on online social networks: collecting global trends on Twitter

    The main purpose of this study, is to propose a suitable methodology to carry out an efficient collecting process via three random strategies: Brownian, Illusion and Reservoir. These random strategies will be applied through a Metropolis-Hastings Random Walk (MHRW). We show that interesting insights can be obtained by sampling emerging global trends on Twitter. The study also offers some important insights providing descriptive statistics and graphical description from the preliminary experiments.


Towards a standard sampling methodology on online social networks: collecting global trends on Twitter
C. A. Pińa-García, Carlos Gershenson and J. Mario Siqueiros-García
Applied Network Science 2016 1:3
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==============================================
Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

You can contribute to Complexity Digest selecting one of our topics (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=d8c7cecde0&e=55e25a0e3e ) and using the "Suggest" button.
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