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

don't shoot me. I am just the mesenger
seriously, I do this culling very quickly, using my own wonderful taste.
That means, I may leave in stuff that doesn't appeal to YOU - just skip 
over
That appeals to NO ONE - just skip
Or I leave out useful stuff. I would be the opposite of insulted if you 
went thru the full Complexity Digest yourself and added to Socnet items 
that I should have included.

No one is complaining, but just wanted to clarify.

   Barry Wellman
  _______________________________________________________________________
   FRSC                 INSNA Founder               University of Toronto
   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 $14  Kindle $9
   _______________________________________________________________________


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Date: Mon, 29 Jun 2015 11:03:24 +0000
From: "[utf-8] Complexity Digest" <[log in to unmask]>
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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=72272aaadf&e=55e25a0e3e


From seconds to months: an overview of multi-scale dynamics of mobile telephone calls

    Big Data on electronic records of social interactions allow approaching human behaviour and sociality from a quantitative point of view with unforeseen statistical power. Mobile telephone Call Detail Records (CDRs), automatically collected by telecom operators for billing purposes, have proven especially fruitful for understanding one-to-one communication patterns as well as the dynamics of social networks that are reflected in such patterns. We present an overview of empirical results on the multi-scale dynamics of social dynamics and networks inferred from mobile telephone calls. We begin with the shortest timescales and fastest dynamics, such as burstiness of call sequences between individuals, and ˙˙zoom out˙˙ towards longer temporal and larger structural scales, from temporal motifs formed by correlated calls between multiple individuals to long-term dynamics of social groups. We conclude this overview with a future outlook.

From seconds to months: an overview of multi-scale dynamics of mobile telephone calls
Jari Saramäki and Esteban Moro

Eur. Phys. J. B (2015) 88: 164
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Hierarchical networks of scientific journals

    Scientific journals are the repositories of the gradually accumulating knowledge of mankind about the world surrounding us. Just as our knowledge is organised into classes ranging from major disciplines, subjects and fields to increasingly specific topics, journals can also be categorised into groups using various metrics. In addition to the set of topics characteristic for a journal, they can also be ranked regarding their relevance from the point of overall influence. One widespread measure is impact factor, but in the present paper we intend to reconstruct a much more detailed description by studying the hierarchical relations between the journals based on citation data. We use a measure related to the notion of m-reaching centrality and find a network which shows the level of influence of a journal from the point of the direction and efficiency with which information spreads through the network. We can also obtain an alternative network using a suitably modified nested
hierarchy extraction method applied to the same data. The results are weakly methodology-dependent and reveal non-trivial relations among journals. The two alternative hierarchies show large similarity with some striking differences, providing together a complex picture of the intricate relations between scientific journals.

Hierarchical networks of scientific journals
Gergely Palla, Gergely Tibély, Enys Mones, Péter Pollner, Tamás Vicsek

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Modeling Opinion Dynamics in Diffusion Networks

    Social media and social networking sites have become a global pinboard for exposition and discussion of news, topics, and ideas, where social media users increasingly form their opinion about a particular topic by learning information about it from her peers. In this context, whenever a user posts a message about a topic, we observe a noisy estimate of her current opinion about it but the influence the user may have on other users' opinions is hidden. In this paper, we introduce a probabilistic modeling framework of opinion dynamics, which allows the underlying opinion of a user to be modulated by those expressed by her neighbors over time. We then identify a set of conditions under which users' opinions converge to a steady state, find a linear relation between the initial opinions and the opinions in the steady state, and develop an efficient estimation method to fit the parameters of the model from historical fine-grained opinion and information diffusion event data.
Experiments on data gathered from Twitter, Reddit and Amazon show that our model provides a good fit to the data and more accurate predictions than alternatives.

Modeling Opinion Dynamics in Diffusion Networks
Abir De, Isabel Valera, Niloy Ganguly, Sourangshu Bhattacharya, Manuel Gomez Rodriguez

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Measuring Emotional Contagion in Social Media

    Social media are used as main discussion channels by millions of individuals every day. The content individuals produce in daily social-media-based micro-communications, and the emotions therein expressed, may impact the emotional states of others. A recent experiment performed on Facebook hypothesized that emotions spread online, even in absence of non-verbal cues typical of in-person interactions, and that individuals are more likely to adopt positive or negative emotions if these are over-expressed in their social network. Experiments of this type, however, raise ethical concerns, as they require massive-scale content manipulation with unknown consequences for the individuals therein involved. Here, we study the dynamics of emotional contagion using Twitter. Rather than manipulating content, we devise a null model that discounts some confounding factors (including the effect of emotional contagion). We measure the emotional valence of content the users are exposed to
before posting their own tweets. We determine that on average a negative post follows an over-exposure to 4.34% more negative content than baseline, while positive posts occur after an average over-exposure to 4.50% more positive contents. We highlight the presence of a linear relationship between the average emotional valence of the stimuli users are exposed to, and that of the responses they produce. We also identify two different classes of individuals: highly and scarcely susceptible to emotional contagion. Highly susceptible users are significantly less inclined to adopt negative emotions than the scarcely susceptible ones, but equally likely to adopt positive emotions. In general, the likelihood of adopting positive emotions is much greater than that of negative emotions.

Measuring Emotional Contagion in Social Media
Emilio Ferrara, Zeyao Yang

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Transitions between homophilic and heterophilic modes of cooperation

    Cooperation is ubiquitous in biological and social systems. Previous studies revealed that a preference toward similar appearance promotes cooperation, a phenomenon called tag-mediated cooperation or communitarian cooperation. This effect is enhanced when a spatial structure is incorporated, because space allows agents sharing an identical tag to regroup to form locally cooperative clusters. In spatially distributed settings, one can also consider migration of organisms, which has a potential to further promote evolution of cooperation by facilitating spatial clustering. However, it has not yet been considered in spatial tag-mediated cooperation models. Here we show, using computer simulations of a spatial model of evolutionary games with organismal migration, that tag-based segregation and homophilic cooperation arise for a wide range of parameters. In the meantime, our results also show another evolutionarily stable outcome, where a high level of heterophilic cooperation is
maintained in spatially well-mixed patterns. We found that these two different forms of tag-mediated cooperation appear alternately as the parameter for temptation to defect is increased.

Transitions between homophilic and heterophilic modes of cooperation
Genki Ichinose, Masaya Saito, Hiroki Sayama, Hugues Bersini

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Complexity Measurement Based on Information Theory and Kolmogorov Complexity

    In the past decades many definitions of complexity have been proposed. Most of these definitions are based either on Shannon's information theory or on Kolmogorov complexity; these two are often compared, but very few studies integrate the two ideas. In this article we introduce a new measure of complexity that builds on both of these theories. As a demonstration of the concept, the technique is applied to elementary cellular automata and simulations of the self-organization of porphyrin molecules.

Complexity Measurement Based on Information Theory and Kolmogorov Complexity
Leong Ting Lui, Germán Terrazas, Hector Zenil, Cameron Alexander, Natalio Krasnogor
Artificial Life Spring 2015, Vol. 21, No. 2: 205˙˙224.

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Extreme multistability: Attractor manipulation and robustness

    The coexistence of infinitely many attractors is called extreme multistability in dynamical systems. In coupled systems, this phenomenon is closely related to partial synchrony and characterized by the emergence of a conserved quantity. We propose a general design of coupling that leads to partial synchronization, which may be a partial complete synchronization or partial antisynchronization and even a mixed state of complete synchronization and antisynchronization in two coupled systems and, thereby reveal the emergence of extreme multistability. The proposed design of coupling has wider options and allows amplification or attenuation of the amplitude of the attractors whenever it is necessary. We demonstrate that this phenomenon is robust to parameter mismatch of the coupled oscillators.

Extreme multistability: Attractor manipulation and robustness
Chittaranjan Hens, Syamal K. Dana, Ulrike Feudel
Chaos 25, 053112 (2015)

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When slower is faster

    The slower is faster (SIF) effect occurs when a system performs worse when its components try to be better. Thus, a moderate individual efficiency actually leads to a better systemic performance. The SIF effect takes place in a variety of phenomena. We review studies and examples of the SIF effect in pedestrian dynamics, vehicle traffic, traffic light control, logistics, public transport, social dynamics, ecological systems, and adaptation. Drawing on these examples we generalize common features of the SIF effect and suggest possible future lines of research.

When slower is faster
Carlos Gershenson, Dirk Helbing

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Lévy walks

    Random walk is a fundamental concept with applications ranging from quantum physics to econometrics. Remarkably, one specific model of random walks appears to be ubiquitous across many fields as a tool to analyze transport phenomena in which the dispersal process is faster than dictated by Brownian diffusion. The Lévy-walk model combines two key features, the ability to generate anomalously fast diffusion and a finite velocity of a random walker. Recent results in optics, Hamiltonian chaos, cold atom dynamics, biophysics, and behavioral science demonstrate that this particular type of random walk provides significant insight into complex transport phenomena. This review gives a self-consistent introduction to Lévy walks, surveys their existing applications, including latest advances, and outlines further perspectives.

Lévy walks
V. Zaburdaev, S. Denisov, and J. Klafter
Rev. Mod. Phys. 87, 483

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John Forbes Nash Jr. (1928˙˙2015)

    In the fall of 1949, many graduate students at Princeton University were assigned rooms in the Graduate College. In one suite, John Nash inhabited a single room, while I shared the double with Lloyd Shapley. John and Lloyd were the mathematicians and I was the economist, and together we pursued our interest in game theory. John was one of the youngest students at the Graduate College. He was from West Virginia, where his father was an engineer and his mother a Latin teacher. He graduated from the Carnegie Institute of Technology with bachelor's and master's degrees in mathematics, and arrived at the math department in Princeton in 1948.

John Forbes Nash Jr. (1928˙˙2015)
Martin Shubik

Science 19 June 2015:
Vol. 348 no. 6241 p. 1324
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Quantifying Loss of Information in Network-based Dimensionality Reduction Techniques

    To cope with the complexity of large networks, a number of dimensionality reduction techniques for graphs have been developed. However, the extent to which information is lost or preserved when these techniques are employed has not yet been clear. Here we develop a framework, based on algorithmic information theory, to quantify the extent to which information is preserved when network motif analysis, graph spectra and spectral sparsification methods are applied to over twenty different biological and artificial networks. We find that the spectral sparsification is highly sensitive to high number of edge deletion, leading to significant inconsistencies, and that graph spectral methods are the most irregular, capturing algebraic information in a condensed fashion but largely losing most of the information content of the original networks. However, the approach shows that network motif analysis excels at preserving the relative algorithmic information content of a network, hence
validating and generalizing the remarkable fact that despite their inherent combinatorial possibilities, local regularities preserve information to such an extent that essential properties are fully recoverable across different networks to determine their family group to which they belong to (eg genetic vs social network). Our algorithmic information methodology thus provides a rigorous framework enabling a fundamental assessment and comparison between different data dimensionality reduction methods thereby facilitating the identification and evaluation of the capabilities of old and new methods.

Quantifying Loss of Information in Network-based Dimensionality Reduction Techniques
Hector Zenil, Narsis A. Kiani, Jesper Tegnér

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Spurious Correlations: Tyler Vigen

    Military intelligence analyst and Harvard Law student Tyler Vigen illustrates the golden rule that "correlation does not equal causation" through hilarious graphs inspired by his viral website.

Is there a correlation between Nic Cage films and swimming pool accidents? What about beef consumption and people getting struck by lightning? Absolutely not. But that hasn't stopped millions of people from going to tylervigen.com and asking, "Wait, what?" Vigen has designed software that scours enormous data sets to find unlikely statistical correlations. He began pulling the funniest ones for his website and has since gained millions of views, hundreds of thousands of likes, and tons of media coverage. Subversive and clever, Spurious Correlations is geek humor at its finest, nailing our obsession with data and conspiracy theory.

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PhD opportunities at MIT in Sustainability of Complex Ecological Networks 

    Ecological networks are the description of interacting populations of different biological species sharing the same geographical area and time. These networks are characterized by temporal changes and constitute the skeleton of biodiversity and natural resources.  Are you interested in understanding how ecological networks respond to environmental changes? Are you interested in engineering quantitative tools to assess how ecological networks are changing and will change? How can we design sustainable strategies to increase the likelihood of persistence of ecological networks subject to biotic and abiotic variations?
Our work is quantitatively and computationally inclined sustained by field ecological data

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