Thanks to the popularity of everything from social media sites such as Twitter to email to mobile phones, it’s easier than ever to get data about who’s connected to whom. With the right tools, we can apply it solve certain problems faster and easier than ever.
Aspiring heirs to the Klout throne, for example, might look to a project called STINGER currently under development at Georgia Tech University. STINGER, which stands for Spatio-Temporal Interaction Networks and Graphs Extensible Representation, is a graph-processing engine that project lead David Bader says is bigger, faster and more flexible than anything currently in use for analyzing social media connections. You provide a shared-memory computing system, and it provides an open-source tool that can help detect relationships between billions of people, places and things as those relationships change over time — even in real time.
Someone using Facebook data, for example, might write an algorithm using where people or pages would be the vertices and actions (likes, shares, wall posts, etc.) would be the graph’s edges. One relatively easy application, Bader explained, would be to analyze how activity around particular people is increasing, decreasing or changing, therefore indicating changes in their importance or the growth of new communities.