The information we experience online comes to us continuously over time, assembled from many small pieces, and conveyed through our social networks. This merging of information, network structure, and flow over time requires new ways of reasoning about the large-scale behavior of information networks. Professor Jure Leskovec will discuss a set of approaches for tracking and predicting how information travels and mutates in online networks. Based on collecting more than 20 million blog posts and news media articles per day, he will discuss how to mine such data to capture and model temporal patterns in the news over a daily time-scale -- in particular, the succession of story lines that evolve and compete for attention. He will also discuss models to quantify the influence of individual media sites on the popularity of news stories and algorithms for inferring hidden networks of information flow.