Guest lecturer: Prof. Title: Jure Leskovec Abstract: 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. I will discuss a set of approaches for tracking information as it travels and mutates in online networks. We show how to capture temporal patterns in the news over a daily time-scale -- in particular, the succession of story lines that evolve, compete for attention, and collectively produce an effect that commentators refer to as the news cycle. I will also discuss a mathematical model to quantify the influence of individual media sites on the popularity of stories and an algorithm for identifying latent social and information diffusion networks.