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   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
   _______________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 1 Jun 2015 11:07:24 +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=9621104c24&e=55e25a0e3e



Machine intelligence

    Research in the field of machine intelligence is seeing a resurgence. 
Big conceptual breakthroughs in artificial neural networks and access to 
powerful processors have led to applications that can process information 
in a human-like way. In addition, the creation of robots that can safely 
assist us with different tasks may soon become a reality. The Reviews in 
this Insight discuss the exciting developments in these fields and the 
opportunities for further research.

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The mortality of companies

    The firm is a fundamental economic unit of contemporary human societies. Studies on the general quantitative and statistical character of firms have produced mixed results regarding their lifespans and mortality. We examine a comprehensive database of more than 25 000 publicly traded North American companies, from 1950 to 2009, to derive the statistics of firm lifespans. Based on detailed survival analysis, we show that the mortality of publicly traded companies manifests an approximately constant hazard rate over long periods of observation. This regularity indicates that mortality rates are independent of a company's age. We show that the typical half-life of a publicly traded company is about a decade, regardless of business sector. Our results shed new light on the dynamics of births and deaths of publicly traded companies and identify some of the necessary ingredients of a general theory of firms.

Madeleine I. G. Daepp , Marcus J. Hamilton , Geoffrey B. West , Luís M. A. Bettencourt. The mortality of companies. Royal Society Interface, 2015 http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=3a09f3468b&e=55e25a0e3e ;

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Analytical Computation of the Epidemic Threshold on Temporal Networks

    The time variation of contacts in a networked system may fundamentally alter the properties of spreading processes and affect the condition for large-scale propagation, as encoded in the epidemic threshold. Despite the great interest in the problem for the physics, applied mathematics, computer science, and epidemiology communities, a full theoretical understanding is still missing and currently limited to the cases where the time-scale separation holds between spreading and network dynamics or to specific temporal network models. We consider a Markov chain description of the susceptible-infectious-susceptible process on an arbitrary temporal network. By adopting a multilayer perspective, we develop a general analytical derivation of the epidemic threshold in terms of the spectral radius of a matrix that encodes both network structure and disease dynamics. The accuracy of the approach is confirmed on a set of temporal models and empirical networks and against numerical
results. In addition, we explore how the threshold changes when varying the overall time of observation of the temporal network, so as to provide insights on the optimal time window for data collection of empirical temporal networked systems. Our framework is of both fundamental and practical interest, as it offers novel understanding of the interplay between temporal networks and spreading dynamics.

Analytical Computation of the Epidemic Threshold on Temporal Networks
Eugenio Valdano, Luca Ferreri, Chiara Poletto, and Vittoria Colizza
Phys. Rev. X 5, 021005 (2015)

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Phase transitions in Pareto optimal complex networks

    The organization of interactions in complex systems can be described by 
networks connecting different units. These graphs are useful 
representations of the local and global complexity of the underlying 
systems. The origin of their topological structure can be diverse, 
resulting from different mechanisms including multiplicative processes and 
optimization. In spatial networks or in graphs where cost constraints are 
at work, as it occurs in a plethora of situations from power grids to the 
wiring of neurons in the brain, optimization plays an important part in 
shaping their organization. In this paper we study network designs 
resulting from a Pareto optimization process, where different simultaneous 
constraints are the targets of selection. We analyze three variations on a 
problem finding phase transitions of different kinds. Distinct phases are 
associated to different arrangements of the connections; but the need of 
drastic topological changes does not determine the presence, nor the 
nature of the phase transitions encountered. Instead, the functions under 
optimization do play a determinant role. This reinforces the view that 
phase transitions do not arise from intrinsic properties of a system 
alone, but from the interplay of that system with its external 
constraints.

Phase transitions in Pareto optimal complex networks
Luís F Seoane, Ricard Solé

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Defining and identifying Sleeping Beauties in science

    A Sleeping Beauty (SB) in science refers to a paper whose importance is not recognized for several years after publication. Its citation history exhibits a long hibernation period followed by a sudden spike of popularity. Previous studies suggest a relative scarcity of SBs. The reliability of this conclusion is, however, heavily dependent on identification methods based on arbitrary threshold parameters for sleeping time and number of citations, applied to small or monodisciplinary bibliographic datasets. Here we present a systematic, large-scale, and multidisciplinary analysis of the SB phenomenon in science. We introduce a parameter-free measure that quantifies the extent to which a specific paper can be considered an SB. We apply our method to 22 million scientific papers published in all disciplines of natural and social sciences over a time span longer than a century. Our results reveal that the SB phenomenon is not exceptional. There is a continuous spectrum of delayed
recognition where both the hibernation period and the awakening intensity are taken into account. Although many cases of SBs can be identified by looking at monodisciplinary bibliographic data, the SB phenomenon becomes much more apparent with the analysis of multidisciplinary datasets, where we can observe many examples of papers achieving delayed yet exceptional importance in disciplines different from those where they were originally published. Our analysis emphasizes a complex feature of citation dynamics that so far has received little attention, and also provides empirical evidence against the use of short-term citation metrics in the quantification of scientific impact.

Defining and identifying Sleeping Beauties in science
Qing Ke, Emilio Ferrara, Filippo Radicchi, Alessandro Flammini

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A U.S. Research Roadmap for Human Computation

    The Web has made it possible to harness human cognition en masse to achieve new capabilities. Some of these successes are well known; for example Wikipedia has become the go-to place for basic information on all things; Duolingo engages millions of people in real-life translation of text, while simultaneously teaching them to speak foreign languages; and fold.it has enabled public-driven scientific discoveries by recasting complex biomedical challenges into popular online puzzle games. These and other early successes hint at the tremendous potential for future crowd-powered capabilities for the benefit of health, education, science, and society. In the process, a new field called Human Computation has emerged to better understand, replicate, and improve upon these successes through scientific research. Human Computation refers to the science that underlies online crowd-powered systems and was the topic of a recent visioning activity in which a representative cross-section of
researchers, industry practitioners, visionaries, funding agency representatives, and policy makers came together to understand what makes crowd-powered systems successful. Teams of experts considered past, present, and future human computation systems to explore which kinds of crowd-powered systems have the greatest potential for societal impact and which kinds of research will best enable the efficient development of new crowd-powered systems to achieve this impact. This report summarize the products and findings of those activities as well as the unconventional process and activities employed by the workshop, which were informed by human computation research.

A U.S. Research Roadmap for Human Computation
Pietro Michelucci, Lea Shanley, Janis Dickinson, Haym Hirsh

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High resolution population estimates from telecommunications data

    Spatial variations in the distribution and composition of populations inform urban development, health-risk analyses, disaster relief, and more. Despite the broad relevance and importance of such data, acquiring local census estimates in a timely and accurate manner is challenging because population counts can change rapidly, are often politically charged, and suffer from logistical and administrative challenges. These limitations necessitate the development of alternative or complementary approaches to population mapping. In this paper we develop an explicit connection between telecommunications data and the underlying population distribution of Milan, Italy. We go on to test the scale invariance of this connection and use telecommunications data in conjunction with high-resolution census data to create easily updated and potentially real time population estimates in time and space.

High resolution population estimates from telecommunications data
Rex W Douglass, David A Meyer, Megha Ram, David Rideout and Dongjin Song

EPJ Data Science 2015, 4:4  http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=28127845ab&e=55e25a0e3e ;

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Predicting traffic volumes and estimating the effects of shocks in massive transportation systems

    We propose a new approach to analyzing massive transportation systems that leverages traffic information about individual travelers. The goals of the analysis are to quantify the effects of shocks in the system, such as line and station closures, and to predict traffic volumes. We conduct an in-depth statistical analysis of the Transport for London railway traffic system. The proposed methodology is unique in the way that past disruptions are used to predict unseen scenarios, by relying on simple physical assumptions of passenger flow and a system-wide model for origin˙˙destination movement. The method is scalable, more accurate than blackbox approaches, and generalizable to other complex transportation systems. It therefore offers important insights to inform policies on urban transportation.

Predicting traffic volumes and estimating the effects of shocks in massive transportation systems
Ricardo Silva, Soong Moon Kang, and Edoardo M. Airoldi

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PNAS May 5, 2015 vol. 112 no. 18 5643-5648

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From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics

    Cascades are ubiquitous in various network environments. How to predict these cascades is highly nontrivial in several vital applications, such as viral marketing, epidemic prevention and traffic management. Most previous works mainly focus on predicting the final cascade sizes. As cascades are typical dynamic processes, it is always interesting and important to predict the cascade size at any time, or predict the time when a cascade will reach a certain size (e.g. an threshold for outbreak). In this paper, we unify all these tasks into a fundamental problem: cascading process prediction. That is, given the early stage of a cascade, how to predict its cumulative cascade size of any later time? For such a challenging problem, how to understand the micro mechanism that drives and generates the macro phenomenons (i.e. cascading proceese) is essential. Here we introduce behavioral dynamics as the micro mechanism to describe the dynamic process of a node's neighbors get infected
by a cascade after this node get infected (i.e. one-hop subcascades). Through data-driven analysis, we find out the common principles and patterns lying in behavioral dynamics and propose a novel Networked Weibull Regression model for behavioral dynamics modeling. After that we propose a novel method for predicting cascading processes by effectively aggregating behavioral dynamics, and propose a scalable solution to approximate the cascading process with a theoretical guarantee. We extensively evaluate the proposed method on a large scale social network dataset. The results demonstrate that the proposed method can significantly outperform other state-of-the-art baselines in multiple tasks including cascade size prediction, outbreak time prediction and cascading process prediction.

From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics
Linyun Yu, Peng Cui, Fei Wang, Chaoming Song, Shiqiang Yang

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Beyond Contact Tracing: Community-Based Early Detection for Ebola Response

    The 2014 Ebola outbreak in west Africa raised many questions about the control of infectious disease in an increasingly connected global society. Limited availability of contact information has made contact tracing difficult or impractical in combating the outbreak. We consider the development of multi-scale public health strategies and simulate policies for community-level response aimed at early screening of communities rather than individuals, as well as travel restrictions to prevent community cross-contamination. Our analysis shows community screening to be effective even at a relatively low level of compliance. In our simulations, 40% of individuals conforming to this policy is enough to stop the outbreak. Simulations with a 50% compliance rate are consistent with the case counts in Liberia during the period of rapid decline after mid September, 2014. We also find the travel restriction policies to be effective at reducing the risks associated with compliance
substantially below the 40% level, shortening the outbreak and enabling efforts to be focused on affected areas. Our results suggest that the multi-scale approach could be applied to help end the outbreaks in Guinea and Sierra Leone, and the generality of our model can be used to further evolve public health strategy for defeating emerging epidemics.

D. Cooney, V. Wong, Y. Bar-Yam, Beyond contact tracing: Community-based early detection for Ebola response, ArXiv:1505.07020 [physics.soc-ph] (May 26, 2014); New England Complex Systems Institute Report 15-05-01

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Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

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