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
BioMed Central | Full text | Multi-agent systems in epidemiology: a first step for computational biology in the study of vector-borne disease transmission Philosophy of this model Earlier individual-based systems [14,34] were quite complex and used the computational framework to produce very complicated models. The main target of these models was to make predictions about possible future dynamics of a given disease. Additional file 1. Format: PDF Size: 104KB Download file This file can be viewed with: Adobe Acrobat Reader Our epidemiological framework was inspired by the classical model proposed first by Kermarck and McK-endrick  and most popularized by Anderson and May . This model could be analyzed in both ways : (i) a conceptual way to study, for instance, the structure of spatio-temporal dynamics of vector-borne diseases and (ii) an applied way by integrating real data, from a GIS for instance, which allow us to track, and eventually to predict, the spatio-temporal dynamics of a given disease in a given environment, like West Nile Fever in Southern France for instance. Components of the multi-agent system Figure 1. Parasite Host
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
Spatio-temporal model of avian influenza spread risk Volume 7, 2011, Pages 104–109 Spatial Statistics 2011: Mapping Global Change Edited By Alfred Stein, Edzer Pebesma and Gerard Heuvelink Abstract HPAI virus has caused significant economic losses in the poultry industry. Backyard and outdoor poultry farms (BOPF) can play an important role in the spread of the disease. Keywords spatial analysis; avian influenza; risk factors; modelling diseases; multicriteria decision; scan statistics References Conclusions of Council of the European Union about Animal Disease Surveillance systems in the EU Seminar Conclusions. 9547/10. D.E.
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.
model transmission vectorielle, application écon Abstract The paper presents the optimal control applied to a vector borne disease with direct transmission in host population. First, we show the existence of the control problem and then use both analytical and numerical techniques to investigate that there are cost effective control efforts for prevention of direct and indirect transmission of disease. Keywords Epidemic model; Optimal control; Pontryagin’s Maximum Principle; Numerical simulation Copyright © 2011 Elsevier Ltd.
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
bubbl.us | brainstorm and mind map online 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 .
Home | Santa Fe Institute Social Network Analysis & an Introduction to Tools Measurefest: network mapping and visualising relative influence - Brilliant Noise Last week I spoke at Measurefest, a conference dedicated to analytics, marketing measurement and CRO. The topic of my talk was, “Network mapping and visualising relative influence”. In case you missed it, here it is in blog form… We need to move on from basic influencer identification based on Twitter bios, to finding people based on their network connections.Drawing networks on NodeXL can visually communicate the meaning of relevance in influencer identification to senior stakeholders.And we can use conversational data from influencer networks to inform and evaluate content strategy. Let’s look at basic influencer identification first. Followerwonk is one of the tools we use to get lists of tweeters based on bio keyword, location and social authority. So when we do basic influencer mapping like this, the results are useful. However, this initial list doesn’t necessarily give us people who are influential around the key topic. Say we take all the followers of @measurefest. Inform: