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GUESS: The Graph Exploration System The Rise of Visual Browsing This time last year at the annual TED conference, Microsoft Live Labs demoed an immersive media-browsing tool that literally caused gasps in the audience. Seadragon/Photosynth is exactly the kind of ’3-D web’ experience people were hyping in the late 1990s, along with VRML (Virtual Reality Modeling Language), as though they were poised to go mainstream. Instead, many users rejected the over-use of tools like Flash, while Google’s simple text-based results became the gold standard. Discussion of ‘visual browsing’ has heated up again, though. It will be interesting to see what kinds of interactions the public adapts to and which they reject. Here are more recent examples I’ve been seeing, some genuinely useful, some experimental and less practical. Cover Flow In late 2004, Andrew Enright first designed a visual browsing method which later became Apple’s Cover Flow. Enright was nostalgic for flipping through the used bins at record stores and mentally cataloging the albums based on cover art:

Large-scale RDF Graph Visualization Tools AI3 Assembles 26 Candidate Tools The pending UMBEL subject concept “backbone” ontology will involve literally thousands of concepts. In order to manage and view such a large structure, a concerted effort to find suitable graph visualization software was mounted. This post presents the candidate listing, as well as some useful starting resources and background information. A subsequent post will present the surprise winner of our evaluation. Starting Resources See Various Example Visualizations For grins, you may also like to see various example visualizations, most with a large-graph bent: Software Options Here is the listing of 26 candidate graph visualization programs assembled to date: Cytoscape – this tool, based on GINY and Piccolo (see below), is under active use by the bioinformatics community and highly recommended by GINY implements a very innovative system for sub-graphing and allows for stunning visuals. headline: alternativeHeadline:

Data Visualization Software | Tulip Gephi, an open source graph visualization and manipulation software Xiaoji Chen's Design Weblog » Power Chart of Chinese Provinces Economist just posts an interactive visualization Chinese Equivalents on their website. It’s a very interesting approach. (Somehow I feel it has an psychological side-effect by saying one province is equivalent to France while it’s neighbor is equivalent to Kenya, though noted in terms of population.) I got curious how we can visualize how actually important the Chinese provinces are. Newman’s code on his website deals with raster image only. You can recognize in this map how unbalanced China is – the west is barely occupied due to challenging natural environment, and population keeps flowing from the middle towards the economic centers (Beijing and the southeast coast). What about looking at the provinces from a social network’s perspective? Tools used: Processing, Tulip, Illustrator Similar Posts

Downloads Gephi is an open-source and multiplatform software distributed under the dual license CDDL 1.0 and GNU General Public License v3. Official Releases Release Notes | System Requirements | Installation instructions Gephi 0.9.2 is the latest stable release. Download Gephi for LinuxVersion 0.9.2 If you have an older Gephi on your computer, you should uninstall it first, see the installation instructions. All downloads:Download Gephi 0.9.2 for Mac OS XDownload Gephi 0.9.2 for WindowsDownload Gephi 0.9.2 for LinuxDownload Gephi 0.9.2 sourcesDownload Older Versions Sources: Gephi uses GitHub to host the source code and track issues. Localization Localization is available in French, Spanish, Japanese, Brazilian Portuguese, Russian, Chinese, Czech and German. Data sets Web/Internet, Social Networks, Biological Networks, Infrastructures and others… If you are looking for data samples to test Gephi, look at the wiki. Learn how to use Gephi Thank you for your support!

Health InfoScape When you have heartburn, do you also feel nauseous? Or if you're experiencing insomnia, do you tend to put on a few pounds, or more? By combing through 7.2 million of our electronic medical records, we have created a disease network to help illustrate relationships between various conditions and how common those connections are. Take a look by condition or condition category and gender to uncover interesting associations. About this data The information used for this visualization is based on 7.2 million patient records from GE's proprietary database, and represents some of the conditions that commonly affect Americans today. Share Downloads Download Application Design Partner MIT SENSEable City Lab

Welkin What is this? Welkin is a graph-based RDF visualizer. What's New in Version 1.1 Works on Windows, Linux and MacOSX. Ok, how do I run it? The easiest way is to run Welkin thru Java WebStart. If the application doesn't start when you click the link above, you don't have Java WebStart installed in your machine. Cool, now what? Welkin visualizes RDF models. A word of warning: above 1000 nodes, real-time drawing performance degrades dramatically even on beefy machines. How can I learn more about it? The best way is to read the Welkin User Guide. Where do I download it? You can obtain Welkin in two different ways: In case you want to download the files from the repository (for example, if you want to have the latest and greatest development snapshot), you need to have a Subversion client installed. svn co welkin at the command line and the latest welkin distribution will appear in the "welkin" directory. Licensing and legal issues Credits

Xiaoji Chen's Design Weblog » Health Infoscape Senseable City Lab partnered with GE to create new ways of understanding human health. Our team created a disease network by analyzing data from over 7.2 million anonymized electronic medical records, taken from between January 2005 and July 2010, across the United States. Barabasi’s lab has published their disease networks generated by genetic similarity in 2007. In our first attempt, diseases/disorders are considered associated if a patient has got them at the same time or sequentially. The resulting network gives us new insight as to how closely connected some seemingly un-related health conditions might be. I made this interactive map for the general public to browse the data. It is a huge network. The network vis was made with Flex and the visualization library Flare, and the user interface with Flash CS4. Team: (Senseable) Carlo Ratti, Eric Baczuk, Dominik Dahlem, Xiaoji Chen (General Electric) Camille Kubie, Aimee Atkinson Tools used: Flash, Flex, Flare, R Similar Posts