# Clustering coefficient

In graph theory, a clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. Evidence suggests that in most real-world networks, and in particular social networks, nodes tend to create tightly knit groups characterised by a relatively high density of ties; this likelihood tends to be greater than the average probability of a tie randomly established between two nodes (Holland and Leinhardt, 1971;[1] Watts and Strogatz, 1998[2]). Two versions of this measure exist: the global and the local. The global version was designed to give an overall indication of the clustering in the network, whereas the local gives an indication of the embeddedness of single nodes. Global clustering coefficient The global clustering coefficient is based on triplets of nodes. Watts and Strogatz defined the clustering coefficient as follows, "Suppose that a vertex has neighbours; then at most edges can exist between them (this occurs when every neighbour of ). over all Related:  SNAentreprise sociale

Visualising Twitter Friend Connections Using Gephi: An Example Using the @WiredUK Friends Network To corrupt a well known saying, “cook a man a meal and he’ll eat it; teach a man a recipe, and maybe he’ll cook for you…”, I thought it was probably about time I posted the recipe I’ve been using for laying out Twitter friends networks using Gephi, not least because I’ve been generating quite a few network files for folk lately, giving them copies, and then not having a tutorial to point them to. So here’s that tutorial… The starting point is actually quite a long way down the “how did you that?” Here’s what we’ll be working towards: a diagram that shows how the people on Twitter that @wiredUK follows follow each other: The tool we’re going to use to layout this graph from a data file is a free, extensible, open source, cross platform Java based tool called Gephi. From the Gephi file menu, Open the appropriate graph file: Import the file as a Directed Graph: The Graph window displays the graph in a raw form: Sometimes a graph may contain nodes that are not connected to any other nodes.

2014 Global Social Impact House — Social Impact House How to apply Student enrolled in the Social Entrepreneurship Course on Coursera can apply by completing this form by October 24, 2014. Finalists will be notified on October 28, 2014. Finalists are selected based on the following criteria: Clarity of their social impact idea; Proven entrepreneurial experience and ability to implement ideas; A clear sense of what skills and resources they currently need to advance their social ventures;A strong Interest in collaborating with other global social entrepreneurs;Passion for making a social impact. Lodging and program are covered by The Center for Social Impact Strategy. For all questions or inquiries, email Cosmo Fujiyama at cosmo@socialimpacthouse.com. Functional brain network efficiency predicts ... [Hum Brain Mapp. 2012

Four Ways of Looking at Twitter - Scott Berinato - Research by Scott Berinato | 2:55 PM February 18, 2010 Data visualization is cool. It’s also becoming ever more useful, as the vibrant online community of data visualizers (programmers, designers, artists, and statisticians — sometimes all in one person) grows and the tools to execute their visions improve. Jeff Clark is part of this community. Clark’s latest work shows much promise. “Twitter is an obvious data source for lots of text information,” says Clark. His tools are definitely early stages, but even now, it’s easy to imagine where they could be taken. Take TwitterVenn. Right away I see Apple tweets are dominating, not surprisingly. I was shocked at the relative infrequency of “google” tweets. So then I went to Twitter Spectrum, a similar tool that compares two search terms and shows which words are most commonly associated with each term and which words are most commonly used in tweets with both terms. I love that the word “ugh” is dead center between Google and Microsoft.

LINC Lab : Learning & Innovation in Networks & Communities - Grenoble Ecole de Management Mission Etudier la manière dont les institutions privées et publiques peuvent puiser au cœur de leurs réseaux et de leurs communautés pour créer du savoir et innover. Pour mener ses travaux de recherche, l'Institut LINC Lab analyse l'impact des communautés et des réseaux sur l'innovation des organisations à travers : leur région d'origine (Europe, USA, Chine) la forme de la structure la culture les secteurs d'activité et les types de projets les nouvelles formes d'organisations ou de réseaux qui émergent Directeur Pr. Dimitris est professeur à Grenoble Ecole de Management, spécialisé dans les systèmes d'information et le management de la technologie. Dimitris est titulaire d'une HDR en sciences économiques de l'Université Pierre Mendès France de Grenoble, d'un doctorat en architecture (Département Ville et aménagement du territoire) de l'Université de Sheffield, en Angleterre, et un diplôme en génie civil de l'Université de Patras, en Grèce. Recherche et Publications

Top 10 Ways to Make Yourself Look (and Be) Smarter How terribly obnoxious of you. Thanks for sharing. Well, it is probably not the smartest move ever to call monolingual people dumb. People in Europe need only travel a few hours to encounter another language, and bi- and tri-linguality is essential to business and communication there. Also, way to be a dick. Yes, I realize that's the norm in Europe, where there's an international border every 50 miles. LH is an American-run site, with a largely American audience and authors who (I believe) are all American, so let's not pretend like suddenly the cultural norms around here are based on what happens in Europe or Canada.

Bridging the Missing Middle: The Impact of Larger Loans on Kiva Today, Kiva entrepreneurs around the world are making fundamental changes in their own lives and the lives of their families -- from a Ugandan farmer looking to buy seeds and tools for increasing crop yields, to a talented seamstress hoping to start a business with a new sewing machine. But some entrepreneurs still face difficulties accessing the capital they need to create jobs and address larger, community-wide issues of poverty. In industrialized nations, these small and medium enterprises (5 - 250 employees) are responsible for 75% of the workforce and 95% of the companies, yet in emerging markets they’re practically non-existent. This lack of small and medium enterprises in less developed countries, an essential part of strong economies, is what's known as the Missing Middle. When an extensive World Bank study asked people living in poverty what they most need, jobs were one of the top answers not being addressed. The Missing Middle Community-Wide Impact