networks and network weaving
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networks and network weaving
How networks can transform our world
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Hierarchy Is Not the Problem… It’s the Power Dynamics

Hierarchy Is Not the Problem… It’s the Power Dynamics | networks and network weaving | Scoop.it
Richard Bartlett provides a more nuanced understanding of hierarchy and power and why this focus on “hierarchy” is a dangerous misdirection
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The wisdom of polarized crowds

The wisdom of polarized crowds | networks and network weaving | Scoop.it
This article explores the effect of ideological polarization on team performance. By analysing millions of edits to Wikipedia, the authors reveal that politically diverse editor teams produce higher-quality articles than homogeneous or moderate teams, and they identify the mechanisms responsible for producing these superior articles.

 

The wisdom of polarized crowds
Feng Shi, Misha Teplitskiy, Eamon Duede & James A. Evans
Nature Human Behaviour (2019)


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The Atlas of Inequality

The Atlas of Inequality | networks and network weaving | Scoop.it
Explore inequality of places, not neighborhoods, using a tool developed at the MIT Media Lab.
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Hierarchy to – Designing Flows for Networks of Purposeful People –

Hierarchy to  – Designing Flows for Networks of Purposeful People – | networks and network weaving | Scoop.it
The question of “designing flows” is pertinent today because the reality of living and working in a networked world is catching up with us. This new reality is catching up with us because being connected and increasingly dependent upon flows of useful information is having a rapidly growing (and deepening) impact upon the way(s) we Read More →
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Weaving a beloved community –

Weaving a beloved community – | networks and network weaving | Scoop.it
by Paula

Network weavers have been quietly renewing communities across the country. Now their approaches are poised to spread.
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[1902.08483] Controlling systemic risk - network structures that minimize it and node properties to calculate it

Evaluation of systemic risk in networks of financial institutions in general
requires information of inter-institution financial exposures. In the framework
of Debt Rank algorithm, we introduce an approximate method of systemic risk
evaluation which requires only node properties, such as total assets and
liabilities, as inputs. We demonstrate that this approximation captures a large
portion of systemic risk measured by Debt Rank. Furthermore, using Monte Carlo
simulations, we investigate network structures that can amplify systemic risk.
Indeed, while no topology in general sense is {\em a priori} more stable if the
market is liquid [1], a larger complexity is detrimental for the overall
stability [2]. Here we find that the measure of scalar assortativity correlates
well with level of systemic risk. In particular, network structures with high
systemic risk are scalar assortative, meaning that risky banks are mostly
exposed to other risky banks. Network structures with low systemic risk are
scalar disassortative, with interactions of risky banks with stable banks.
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Introduction to Social Network Analysis Seminar | Statistical Workshops in Philadelphia | Statistics Training Course and Class | Statistical Horizons

A 2-Day Statistical Seminar on Introduction to Social Network Analysis, taught by Stephen Borgatti, Ph.D in Philadelphia
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Aral Balkan on Building the People's Internet

Aral Balkan on Building the People's Internet | networks and network weaving | Scoop.it
Reposted from NESTA on Youtube.com Listen to Aral Balkan, cyborg rights activist, designer and developer, outline the problem with surveillance capitalism and explain how we can avoid it to build a People’s Internet starting right here in the EU.
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Percolation and the Effective Structure of Complex Networks

Percolation and the Effective Structure of Complex Networks | networks and network weaving | Scoop.it

Analytical approaches to model the structure of complex networks can be distinguished into two groups according to whether they consider an intensive (e.g., fixed degree sequence and random otherwise) or an extensive (e.g., adjacency matrix) description of the network structure. While extensive approaches—such as the state-of-the-art message passing approximation—typically yield more accurate predictions, intensive approaches provide crucial insights on the role played by any given structural property in the outcome of dynamical processes. Here we introduce an intensive description that yields almost identical predictions to the ones obtained with the message passing approximation using bond percolation as a benchmark. Our approach distinguishes nodes according to two simple statistics: their degree and their position in the core-periphery organization of the network. Our near-exact predictions highlight how accurately capturing the long-range correlations in network structures allows easy and effective compression of real complex network data.

 

Percolation and the Effective Structure of Complex Networks
Antoine Allard and Laurent Hébert-Dufresne
Phys. Rev. X 9, 011023 – Published 5 February 2019


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Encouraging Innovation & Collaboration at the Grassroots: GoPhil Seed Grants in Nepal

Encouraging Innovation & Collaboration at the Grassroots: GoPhil Seed Grants in Nepal | networks and network weaving | Scoop.it
GoPhilanthropic is a US-based non-profit that helps grassroots organizations provide access to education, health and human rights in impoverished regions round the world. Co-Founded in 2011, our be…
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Paths to Putin | T N T : The Network Thinkers

Paths to Putin | T N T : The Network Thinkers | networks and network weaving | Scoop.it
The Trump-Russia investigation is not a “ witch ” hunt — it is a “ which ” hunt!  [1] Which organizations or people were key t
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It Takes A Village: Change Management As Community Building

It Takes A Village: Change Management As Community Building | networks and network weaving | Scoop.it
Organizational change is easier to achieve within a strictly hierarchical structure, right? Social network theory tells us otherwise. Diffusion of change relies on network density, and hierarchies have very little of that.
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Segregation in religion networks

Segregation in religion networks | networks and network weaving | Scoop.it

Religion is considered as a notable origin of interpersonal relations, as well as an effective and efficient tool to organize a huge number of people towards some challenging targets. At the same time, a believer prefers to make friend with other people of the same faith, and thus people of different faiths tend to form relatively isolated communities. The segregation between different religions is a major factor for many social conflicts. However, quantitative understanding of religious segregation is rare. Here we analyze a directed social network extracted from weibo.com (the largest directed social network in China, similar to twitter.com), which is consisted of 6875 believers in Christianity, Buddhism, Islam and Taoism. This religion network is highly segregative. Comparative analysis shows that the extent of segregation for different religions is much higher than that for different races and slightly higher than that for different political parties. Furthermore, we study the few cross-religion links and find 46.7% of them are probably related to charitable issues. Our findings provide quantitative insights into religious segregation and valuable evidence for religious syncretism.

 

Segregation in religion networks
Jiantao Hu, Qian-Ming Zhang and Tao Zhou
EPJ Data Science 2019 8:6
https://doi.org/10.1140/epjds/s13688-019-0184-x


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Rare and everywhere: Perspectives on scale-free networks

Rare and everywhere: Perspectives on scale-free networks | networks and network weaving | Scoop.it
Are scale-free networks rare or universal? Important or not? We present the recent research about degree distributions of networks. This is a controversial topic, but, we argue, with some adjustments of the terminology, it does not have to be.
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(1) KUMU Quick Tips: Color coding - YouTube

Kumu now has a bunch of videos to help you get the most from Kumu! Highly recommended.

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The New Art of Making Friends and Finding Community by Julia Hotz —

The New Art of Making Friends and Finding Community by Julia Hotz — | networks and network weaving | Scoop.it
Even as researchers find loneliness is epidemic, people young and old are relearning how to hang out.
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(1) Radical Networks – Building connected communities - YouTube  THIS IS SO IMPORTANT _ PLEASE WATCH

Platforms for "dream grants," participatory decision-making, etc

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Stop the Madness: You can't train others on inclusive networks when your diversity is lacking

Stop the Madness: You can't train others on inclusive networks when your diversity is lacking | networks and network weaving | Scoop.it
I love June Holley.  June has been committed to my success since day 1. Several years ago, we were both on a site created by MIchael Soto, called Spark Collaboration.  June and I were mat…
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as a Member-led Network

as a Member-led Network | networks and network weaving | Scoop.it
In September 2018, network members established the Hive NYC Council through a consensus vote, officially making Hive a member-led network. Formed in 2009, the network has historically been governed and facilitated by an independent steward; first the Social Science Research Council and then, from 2011 until 2017, the Mozilla Foundation. The evolution of Hive NYC …
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Network Weaving FB Group Mapping Invitation

Network Weaving FB Group Mapping Invitation | networks and network weaving | Scoop.it
Sarah Shanahan and Jim Best of the Master Mappers are hosting an Exploratory Meeting about social system mapping of the Network Weaving Facebook Group. WHERE: Please register in advance f…
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[1712.01790] The use of multilayer network analysis in animal behaviour

[1712.01790] The use of multilayer network analysis in animal behaviour | networks and network weaving | Scoop.it

Network analysis has driven key developments in research on animal behaviour
by providing quantitative methods to study the social structures of animal
groups and populations. A recent formalism, known as \emph{multilayer network
analysis}, has advanced the study of multifaceted networked systems in many
disciplines. It offers novel ways to study and quantify animal behaviour as
connected 'layers' of interactions. In this article, we review imal social networks, and we call attention to
methodological challenges for the application of these approaches. Our aim is
to instigate the study of new questions about animal sociality using the new
toolbox of multilayer network analysis.

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