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***** To join INSNA, visit http://www.insna.org *****
A little mellow doing this in Monument Valley.
Barry Wellman
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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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---------- Forwarded message ----------
Date: Mon, 12 Oct 2015 11:04:42 +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=c3ec34ae7c&e=55e25a0e3e
Networkcontrology
An increasing number of complex systems are now modeled as networks of coupled dynamical entities. Nonlinearity and high-dimensionality are hallmarks of the dynamics of such networks but have generally been regarded as obstacles to control. Here, I discuss recent advances on mathematical and computational approaches to control high-dimensional nonlinear network dynamics under general constraints on the admissible interventions. I also discuss the potential of network control to address pressing scientific problems in various disciplines.
Networkcontrology,
Adilson E. Motter,
Chaos 25, 097621 (2015)
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Urban Scaling in Europe
Over the last decades, in disciplines as diverse as economics, geography, and complex systems, a perspective has arisen proposing that many properties of cities are quantitatively predictable due to agglomeration or scaling effects. Using new harmonized definitions for functional urban areas, we examine to what extent these ideas apply to European cities. We show that while most large urban systems in Western Europe (France, Germany, Italy, Spain, UK) approximately agree with theoretical expectations, the small number of cities in each nation and their natural variability preclude drawing strong conclusions. We demonstrate how this problem can be overcome so that cities from different urban systems can be pooled together to construct larger datasets. This leads to a simple statistical procedure to identify urban scaling relations, which then clearly emerge as a property of European cities. We compare the predictions of urban scaling to Zipf's law for the size distribution of
cities and show that while the former holds well the latter is a poor descriptor of European cities. We conclude with scenarios for the size and properties of future pan-European megacities and their implications for the economic productivity, technological sophistication and regional inequalities of an integrated European urban system.
Urban Scaling in Europe
Luis M. A. Bettencourt, Jose Lobo
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Detecting global bridges in networks
The identification of nodes occupying important positions in a network structure is crucial for the understanding of the associated real-world system. Usually, betweenness centrality is used to evaluate a node capacity to connect different graph regions. However, we argue here that this measure is not adapted for that task, as it gives equal weight to "local" centers (i.e. nodes of high degree central to a single region) and to "global" bridges, which connect different communities. This distinction is important as the roles of such nodes are different in terms of the local and global organisation of the network structure. In this paper we propose a decomposition of betweenness centrality into two terms, one highlighting the local contributions and the other the global ones. We call the latter bridgeness centrality and show that it is capable to specifically spot out global bridges. In addition, we introduce an effective algorithmic implementation of this measure and
demonstrate its capability to identify global bridges in air transportation and scientific collaboration networks.
Detecting global bridges in networks
Pablo Jensen, Matteo Morini, Marton Karsai, Tommaso Venturini, Alessandro Vespignani, Mathieu Jacomy, Jean-Philippe Cointet, Pierre Merckle, Eric Fleury
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Exploration and Exploitation of Victorian Science in Darwin's Reading Notebooks
Search in an environment with an uncertain distribution of resources involves a trade-off between local exploitation and distant exploration. This extends to the problem of information foraging, where a knowledge-seeker shifts between reading in depth and studying new domains. To study this, we examine the reading choices made by one of the most celebrated scientists of the modern era: Charles Darwin. Darwin built his theory of natural selection in part by synthesizing disparate parts of Victorian science. When we analyze his extensively self-documented reading we find shifts, on multiple timescales, between choosing to remain with familiar topics and seeking cognitive surprise in novel fields. On the longest timescales, these shifts correlate with major intellectual epochs of his career, as detected by Bayesian epoch estimation. When we compare Darwin's reading path with publication order of the same texts, we find Darwin more adventurous than the culture as a whole.
Exploration and Exploitation of Victorian Science in Darwin's Reading Notebooks
Jaimie Murdock, Colin Allen, Simon DeDeo
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The spatial component of R&D networks
We study the role of geography in R&D networks by means of a quantitative, micro-geographic approach. Using a large database that covers international R&D collaborations from 1984 to 2009, we localize each actor precisely in space through its latitude and longitude. This allows us to analyze the R&D network at all geographic scales simultaneously. Our empirical results show that despite the high importance of the city level, transnational R&D collaborations at large distances are much more frequent than expected from similar networks. This provides evidence for the ambiguity of distance in economic cooperation which is also suggested by the existing literature. In addition we test whether the hypothesis of local buzz and global pipelines applies to the observed R&D network by calculating well-defined metrics from network theory.
The spatial component of R&D networks
Tobias Scholl, Antonios Garas, Frank Schweitzer
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Time-dependent community structure in legislation cosponsorship networks in the Congress of the Republic of Peru
We examine community structure in time-dependent legislation cosponsorship networks in the Peruvian Congress, which we compare briefly to such networks in the United States Congress. To study these legislatures, we employ a multilayer representation of temporal networks, in which each layer connects legislators based on the similarity of their patterns for cosponsoring bills. We then use multilayer modularity maximization to detect communities. From our computations, we are able to capture power shifts in the Peruvian Congress during 2006˙˙2011. For example, we observe the emergence of "opportunists", who switch from one community to another, as well as cohesive legislative communities whose initial component legislators never switch. Interestingly, many of the opportunists belonged to the group that won the majority in Congress.
Time-dependent community structure in legislation cosponsorship networks in the Congress of the Republic of Peru
Sang Hoon Lee, José Manuel Magallanes, Mason A. Porter
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On the similarity of symbol frequency distributions with heavy tails
Quantifying the similarity between symbolic sequences is a traditional problem in Information The- ory which requires comparing the frequencies of symbols in different sequences. In numerous modern applications, ranging from DNA over music to texts, the distribution of symbol frequencies is char- acterized by heavy-tailed distributions (e.g., Zipf˙˙s law). The large number of low-frequency symbols in these distributions poses major difficulties to the estimation of the similarity between sequences, e.g., they hinder an accurate finite-size estimation of entropies. Here we show how the accuracy of estimations depend on the sample size N, not only for the Shannon entropy (˙˙ = 1) and its corresponding similarity measures (e.g., the Jensen-Shanon divergence) but also for measures based on the generalized entropy of order ˙˙. For small ˙˙˙˙s, including ˙˙ = 1, the bias and fluctuations in the estimations decay slower than the 1/N decay observed in short-tailed distributions. For ˙˙
larger than a critical value ˙˙˙˙ ˙˙ 2, the 1/N-scaling is recovered. We show the practical significance of our results by quantifying the evolution of the English language over the last two centuries using a complete ˙˙-spectrum of measures. We find that frequent words change more slowly than less frequent words and that ˙˙ = 2 provides the most robust measure to quantify language change.
On the similarity of symbol frequency distributions with heavy tails
Martin Gerlach, Francesc Font-Clos, Eduardo G. Altmann
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The Silo Effect: The Peril of Expertise and the Promise of Breaking Down Barriers (by Gillian Tett)
One of the characteristics of industrial age enterprises is that they are organized around functional departments. This organizational structure results in both limited information and restricted thinking. The Silo Effect asks these basic questions: why do humans working in modern institutions collectively act in ways that sometimes seem stupid? Why do normally clever people fail to see risks and opportunities that later seem blindingly obvious? Why, as psychologist Daniel Kahneman put it, are we sometimes so ˙˙blind to our own blindness˙˙?
Gillian Tett, journalist and senior editor for the Financial Times, answers these questions by plumbing her background as an anthropologist and her experience reporting on the financial crisis in 2008. In The Silo Effect, she shares eight different tales of the silo syndrome, spanning Bloomberg˙˙s City Hall in New York, the Bank of England in London, Cleveland Clinic hospital in Ohio, UBS bank in Switzerland, Facebook in San Francisco, Sony in Tokyo, the BlueMountain hedge fund, and the Chicago police. Some of these narratives illustrate how foolishly people can behave when they are mastered by silos. Others, however, show how institutions and individuals can master their silos instead. These are stories of failure and success.
From ideas about how to organize office spaces and lead teams of people with disparate expertise, Tett lays bare the silo effect and explains how people organize themselves, interact with each other, and imagine the world can take hold of an organization and lead from institutional blindness to 20/20 vision.
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CompleNet 2016 - 7th Workshop on Complex Networks
http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=5ce3982f4a&e=55e25a0e3e (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=0e69e9424e&e=55e25a0e3e) /
Dijon, France, March 23rd-25th, 2016.
Important dates:
* Abstract/Paper submission deadline: October 30th, 2015
* Notification of acceptance: November 27th, 2015
* Submission of Camera-Ready (papers): December 11th 2015
* Early registration ends on: December 11th 2015
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The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (by Pedro Domingos)
Algorithms increasingly run our lives. They find books, movies, jobs, and dates for us, manage our investments, and discover new drugs. More and more, these algorithms work by learning from the trails of data we leave in our newly digital world. Like curious children, they observe us, imitate, and experiment. And in the world˙˙s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask.
Machine learning is the automation of discovery˙˙the scientific method on steroids˙˙that enables intelligent robots and computers to program themselves. No field of science today is more important yet more shrouded in mystery. Pedro Domingos, one of the field˙˙s leading lights, lifts the veil for the first time to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He charts a course through machine learning˙˙s five major schools of thought, showing how they turn ideas from neuroscience, evolution, psychology, physics, and statistics into algorithms ready to serve you. Step by step, he assembles a blueprint for the future universal learner˙˙the Master Algorithm˙˙and discusses what it means for you, and for the future of business, science, and society.
If data-ism is today˙˙s rising philosophy, this book will be its bible. The quest for universal learning is one of the most significant, fascinating, and revolutionary intellectual developments of all time. A groundbreaking book, The Master Algorithm is the essential guide for anyone and everyone wanting to understand not just how the revolution will happen, but how to be at its forefront.
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International Research in Computational Social Sciences: VolkswagenStiftung
With this call, the Volkswagen Foundation offers financial support for cooperative research projects, both for international workshops and summer schools as well as for cooperative research projects between international postdoctoral researchers.
International Research in Computational Social Sciences
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Complexity, Criticality, and Computation (C3) Research Camp - The University of Sydney
We will consider a diverse range of systems, applications, theoretical and practical approaches to computational modelling of modern complex systems such as health, including information theory, agent-based simulation, network theory, nonlinear dynamics, swarm intelligence, evolutionary methods, computational neuroscience, and econophysics, among others.
Event details
When: 9am - 5pm, 30 November - 4 December
Where: Charles Perkins Centre Seminar Rooms, The University of Sydney
Cost: FEE WAIVERS We have an opportunity to provide a number of fee waivers for the Camp. If you are interested in applying for a waiver please email [log in to unmask] (mailto:[log in to unmask])
* $550 for corporate
* $275 for academics
* $110 for students
* Prices include GST.
General registrations close Friday 30 October
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Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.
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