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Introduction to Network Visualization with GEPHI

Introduction to Network Visualization with GEPHI
New tutorial available! A completely new version of this tutorial has been published, with 2 complete and complementary datasets to learn and explore many basic and advanced features of Gephi: To the new tutorial Gephi workshop at University of Bern (photo Radu Suciu) Social Network Analysis is a lens, a way of looking at reality. Network Analysis appears to be an interesting tool to give the researcher the ability to see its data from a new angle. I propose below, after a short introduction about the basis of SNA and some examples which shows the potential of this tool, a transcript of tutorial given during a workshop of the first Digital Humanities summer school in Switzerland (June 28. 2013), and kept up to date. 1. A network consists of two components : a list of the actors composing the network, and a list of the relations (the interactions between actors). By left, you can observe a very simple social graph, with both lists explicited. 2. 3. GephiDataset(edges)Dataset (nodes) 4. Nodes

Sources And Methods: The Potential of Social Network Analysis in Intelligence (In case you missed our most recent article over at e-International Relations or at OODALoop, we are reprinting it here!)The legality of the National Security Agency’s (NSA’s) use of US citizens’ metadata to identify and track foreign intelligence organizations and their operatives is currently a subject of much debate. Less well understood (and consequently routinely misreported) are the capabilities and limitations of social network analysis, the methodology often used to evaluate this metadata. One of the first causes of confusion is definitional. In addition, the first modern version of what would come to be called social network analysis was developed not by an intelligence agency or computer scientist but by Columbia professor and psychosociologist, Jacob Moreno, in 1934. Figure 2 – Modern social network analysis uses powerful computers and graph theory to map out the relationships between thousands of nodes and hundreds of thousands of links. Identifying New Agents Caveat Emptor

Gephi, an open source graph visualization and manipulation software Mapa por países utilización de Internet | Revista Berlín El uso mundial de internet es un dato que nos puede ofrecer muchos tipos de informaciones sociodemográficas sobre distintos países. Desde hace tiempo, diversas entidades vienen realizando mediciones según los datos aportados por las compañías de telecomunicaciones e instituciones gubernamentales. Acaba de ser presentado a la blogosfera una investigación que sigue unos parámetros muy distintos a los establecidos oficialmente. Hacker investiga el uso regional de internet Según la versión online de la revista alemana Der Spiegel (21/03/2013) un hacker anónimo ha presentado los espectaculares resultados de una investigación sobre el uso de Internet en las distintas regiones del globo. En la imágenes producidas mostrando la intensidad de uso de Internet por países, se puede comprobar que hay una relación directa entre desarrollo económico del país y la densidad de usuarios. La investigación podría ser ilegal Investigación solamente secundaria Espectaculares imágenes del uso por países de Internet

Step by Step Social Network Analysis using Gephi: Getting Started | My exploration in data analytics In continuation to my previous blog post on Social Network Analysis using Gephi, I’m writing this post to explain how do create a very simple social network analysis using Gephi. You can also look at a very good introduction to Gephi written by Martin Grandjean here Goal and Scenario: We have a friends network we want to depict visually how the friends are interconnected with each other. The goal is to understand how to use Gephi Step by step along with having very fundamental understanding of how the data is represented. Pre-Requisites: * You would need the Gephi software which you can download from here. * Data to be imported Step by step Instructions: Step 1: After you install Gephi, you will see a screen like this. Step 2: In this example we are going to import that data from CSV files and we are going to use them for ease of use. Step 3: Once you Open the Data Laboratory pane now you click Import Spreadsheet. Which will result like the following once you click the finish button.

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OECD – Your Better Life Index Average personal index for Germany, men, 15–24 How’s life? There is more to life than the cold numbers of GDP and economic statistics – This Index allows you to compare well-being across countries, based on 11 topics the OECD has identified as essential, in the areas of material living conditions and quality of life. Download executive summary Download the index data Learn more about the index Better Life BlogArchive Happiness pays For World Happiness Day our guest author suggests that money can't buy you happiness but happiness may get you more money. more ... Gender equality in the workplace Over the past twenty years, women have made huge gains in the workplace but full job equality is still far from reality. more ... Education for well-being: Online discussion We know education is an essential component of well-being, so what makes an education that promotes well-being? more ... Visit our blog Blog RSS feed Find Out MoreArchive Mar 19, 2014 Society at a Glance 2014: OECD Social Indicators

Urban Scaling and Its Deviations: Revealing the Structure of Wealth, Innovation and Crime across Cities With urban population increasing dramatically worldwide, cities are playing an increasingly critical role in human societies and the sustainability of the planet. An obstacle to effective policy is the lack of meaningful urban metrics based on a quantitative understanding of cities. Typically, linear per capita indicators are used to characterize and rank cities. However, these implicitly ignore the fundamental role of nonlinear agglomeration integral to the life history of cities. As such, per capita indicators conflate general nonlinear effects, common to all cities, with local dynamics, specific to each city, failing to provide direct measures of the impact of local events and policy. 1.15). Figures Citation: Bettencourt LMA, Lobo J, Strumsky D, West GB (2010) Urban Scaling and Its Deviations: Revealing the Structure of Wealth, Innovation and Crime across Cities. Editor: Juan A. Received: May 18, 2010; Accepted: September 16, 2010; Published: November 10, 2010 Introduction . Results .

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