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)
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 →
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.
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.
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
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…
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.
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.
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.
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…
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 …
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…
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.
Sharing your scoops to your social media accounts is a must to distribute your curated content. Not only will it drive traffic and leads through your content, but it will help show your expertise with your followers.
How to integrate my topics' content to my website?
Integrating your curated content to your website or blog will allow you to increase your website visitors’ engagement, boost SEO and acquire new visitors. By redirecting your social media traffic to your website, Scoop.it will also help you generate more qualified traffic and leads from your curation work.
Distributing your curated content through a newsletter is a great way to nurture and engage your email subscribers will developing your traffic and visibility.
Creating engaging newsletters with your curated content is really easy.
To get content containing either thought or leadership enter:
To get content containing both thought and leadership enter:
To get content containing the expression thought leadership enter:
You can enter several keywords and you can refine them whenever you want. Our suggestion engine uses more signals but entering a few keywords here will rapidly give you great content to curate.
Your new post is loading...
Scoop.it!
