Dynamic VisualizationThe following video shows a sneak peek at our current efforts to dynamically visualize the spreading of news through social networks. This particular example shows the spreading of a popular news story on the Digg social network, but closely reflects an approach we will taking to model our Twitter data once we have collected enough information. While it may be a bit difficult to see (we're working on that), the graph in the video consists of 130,000+ nodes (users), each ring representing the collection of neighbors X-hops away from the initiator of the news story. Any time you see a node turn from white to green, it means they voted for the news submitted by the initiator. Ultimately, we would like to be able to present the dynamic data in a similar fashion to the static snapshot you see further below, however, the technology used to allow user interaction via the web has not yet implemented support for dynamic data.
Static VisualizationThe graph below depicts a static snapshot of sample Twitter data. Using your mouse, you are able to click and drag to traverse the graph, as well as scroll the mouse wheel to zoom in and out. Also, if you hover your mouse over any particular node (user), you are able to see their twitterID, and their 1st hop neighboring nodes will be highlighted. In this particular example, it has been grouped according to how well connected the users are to one another, and colorized according to attributes they have in common (special interests, geographical location, etc).