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   Barry Wellman

   NetLab                        FRSC                      INSNA Founder
   Faculty of Information (iSchool)                 611 Bissell Building
   140 St. George St.    University of Toronto    Toronto Canada M5S 3G6          twitter: @barrywellman
                  NSA/CSEC: Canadian and American citizen
   NETWORKED:The New Social Operating System. Lee Rainie & Barry Wellman
   MIT Press        Print $14  Kindle $16

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Date: Mon, 3 Mar 2014 09:44:29 -0600
From: Complexity Digest Administration <[log in to unmask]>
To: [log in to unmask]
Subject: [comdig] Latest Complexity Digest Posts

Information Evolution in Social Networks

    Social networks readily transmit information, albeit with less than perfect fidelity. We present a large-scale measurement of this imperfect information copying mechanism by examining the dissemination and evolution of thousands of memes, collectively replicated hundreds of millions of times in the online social network Facebook. The information undergoes an evolutionary process that exhibits several regularities. A meme's mutation rate characterizes the population distribution of its variants, in accordance with the Yule process. Variants further apart in the diffusion cascade have greater edit distance, as would be expected in an iterative, imperfect replication process. Some text sequences can confer a replicative advantage; these sequences are abundant and transfer "laterally" between different memes. Subpopulations of the social network can preferentially transmit a specific variant of a meme if the variant matches their beliefs or culture. Understanding the mechanism
driving change in diffusing information has important implications for how we interpret and harness the information that reaches us through our social networks.

Information Evolution in Social Networks
Lada A. Adamic, Thomas M. Lento, Eytan Adar, Pauline C. Ng

Predicting Crowd Behavior with Big Public Data

    With public information becoming widely accessible and shared on today's web, greater insights are possible into crowd actions by citizens and non-state actors such as large protests and cyber activism. We present efforts to predict the occurrence, specific timeframe, and location of such actions before they occur based on public data collected from over 300,000 open content web sources in 7 languages, from all over the world, ranging from mainstream news to government publications to blogs and social media. Using natural language processing, event information is extracted from content such as type of event, what entities are involved and in what role, sentiment and tone, and the occurrence time range of the event discussed. Statements made on Twitter about a future date from the time of posting prove particularly indicative. We consider in particular the case of the 2013 Egyptian coup d'etat. The study validates and quantifies the common intuition that data on social media
(beyond mainstream news sources) are able to predict major events.

Predicting Crowd Behavior with Big Public Data
Nathan Kallus

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Autonomous drones flock like birds

    A Hungarian team has created the first drones that can fly as a coordinated flock. The researchers watched as the ten autonomous robots took to the air in a field outside Budapest, zipping through the open sky, flying in formation or even following a leader, all without any central control.

Autonomous drones flock like birds
Ed Yong

Nature doi:10.1038/nature.2014.14776

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Damage spreading in spatial and small-world random Boolean networks

    The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean networks (RBNs) are commonly used as a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other nonrandom connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the Hamming distance at very low connectivities ($\bar{K} << 1$) and that the critical connectivity of stability $\bar{K}$ changes compared to random networks. At higher $\bar{K}$, this scaling remains unchanged.
We also show that the Hamming distance of spatially local networks scales with a power law as the system size $N$ increases, but with a different exponent for local and small-world networks. The scaling arguments for small-world networks are obtained with respect to the system sizes and strength of spatially local connections. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.

Qiming Lu and Christof Teuscher
Damage spreading in spatial and small-world random Boolean networks
Phys. Rev. E 89, 022806 (2014)

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 Alessandro Vespignani on theoretical developments for complex networks and systems

    This interview with Alessandro Vespignani is about the future of modelling and forecasting of epidemics and is part of the Futurium Talking Futures interview series. More information is available here:

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 Dirk Helbing on complexity in economic theory

    This interview with Dirk Helbing on the Future of the economy is part of the Futurium Talking Futures interview series. More information is available here:

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Introduction to Computational Social Science: Principles and Applications (by Claudio Cioffi-Revilla)

    This reader-friendly textbook is the first work of its kind to provide a unified Introduction to Computational Social Science (CSS). Four distinct methodological approaches are examined in detail, namely automated social information extraction, social network analysis, social complexity theory and social simulation modeling. The coverage of these approaches is supported by a discussion of the historical context, as well as by a list of texts for further reading. Features: highlights the main theories of the CSS paradigm as causal explanatory frameworks that shed new light on the nature of human and social dynamics; explains how to distinguish and analyze the different levels of analysis of social complexity using computational approaches; discusses a number of methodological tools; presents the main classes of entities, objects and relations common to the computational analysis of social complexity; examines the interdisciplinary integration of knowledge in the context of
social phenomena.

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Networks of Networks: The Last Frontier of Complexity (by Gregorio D'Agostino and Antonio Scala)

    The present work is meant as a reference to provide an organic and comprehensive view of the most relevant results in the exciting new field of Networks of Networks (NetoNets). Seminal papers have recently been published posing the basis to study what happens when different networks interact, thus providing evidence for the emergence of new, unexpected behaviors and vulnerabilities. From those seminal works, the awareness on the importance understanding Networks of Networks (NetoNets) has spread to the entire community of Complexity Science. The reader will benefit from the experience of some of the most well-recognized leaders in this field. The contents have been aggregated under four headings; General Theory, Phenomenology, Applications and Risk Assessment. The reader will be impressed by the different applications of the general paradigm that span from physiology, to financial risk, to transports. We are currently making the first steps to reduce the distance between the
language and the way of thinking of the two communities of experts in real infrastructures and the complexity scientists. Although this path may prove to be long, it is extremely promising, both in extending our understanding of complex systems and in finding concrete applications that can enhance the life quality of millions of people.

IN 1973

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Postdoc, ETH Zurich

    Postdoc with a strongbackground in computer science to strengthen our interdisciplinary team in the areas of Web science, Big Data analytics, smartphone platforms, and distributed computing. The goals of this project include designing a "Planetary Nervous System" that would give us a detailed real-time view of the world, crawling and analyzing web content on a large scale, and establishing a smartphone platform for gathering scientific data. The successful applicant is expected to actively interact with other team members from different disciplines and to acquire a high-level understanding of the general topics in quantitative social sciences, sociophysics, and related areas.

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Editor-in-Chief: Carlos Gershenson.

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