Top Interactive Agency – Personalized Marketing Solutions 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
Twitter's original drawing 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
The original proposal of the WWW, HTMLized A hand conversion to HTML of the original MacWord (or Word for Mac?) document written in March 1989 and later redistributed unchanged apart from the date added in May 1990. Provided for historical interest only. This document was an attempt to persuade CERN management that a global hypertext system was in CERN's interests. Other versions which are available are: ©Tim Berners-Lee 1989, 1990, 1996, 1998. This proposal concerns the management of general information about accelerators and experiments at CERN. Overview Many of the discussions of the future at CERN and the LHC era end with the question - ªYes, but how will we ever keep track of such a large project? It then summarises my short experience with non-linear text systems known as ªhypertextº, describes what CERN needs from such a system, and what industry may provide. Losing Information at CERN CERN is a wonderful organisation. A problem, however, is the high turnover of people. Where is this module used? Linked information systems
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
How to publish Linked Data on the Web This document provides a tutorial on how to publish Linked Data on the Web. After a general overview of the concept of Linked Data, we describe several practical recipes for publishing information as Linked Data on the Web. This tutorial has been superseeded by the book Linked Data: Evolving the Web into a Global Data Space written by Tom Heath and Christian Bizer. This tutorial was published in 2007 and is still online for historical reasons. The Linked Data book was published in 2011 and provides a more detailed and up-to-date introduction into Linked Data. The goal of Linked Data is to enable people to share structured data on the Web as easily as they can share documents today. The term Linked Data was coined by Tim Berners-Lee in his Linked Data Web architecture note. Applying both principles leads to the creation of a data commons on the Web, a space where people and organizations can post and consume data about anything. This chapter describes the basic principles of Linked Data.
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.
MediaWiki 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.
Derrick de Kerckhove Derrick de Kerckhove (born 1944) is the author of The Skin of Culture and Connected Intelligence and Professor in the Department of French at the University of Toronto, Canada. He was the Director of the McLuhan Program in Culture and Technology from 1983 until 2008. In January 2007, he returned to Italy for the project and Fellowship “Rientro dei cervelli”, in the Faculty of Sociology at the University of Naples Federico II where he teaches "Sociologia della cultura digitale" and "Marketing e nuovi media". He was invited to return to the Library of Congress for another engagement in the Spring of 2008. He is research supervisor for the PhD Planetary Collegium M-node directed by Francesco Monico. Background De Kerckhove received his Ph.D in French Language and Literature from the University of Toronto in 1975 and a Doctorat du 3e cycle in Sociology of Art from the University of Tours (France) in 1979. Publications Other works References External links
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.