Abstract: In this talk we present current and ongoing work about how to learn the Lego-like building blocks of real world networks in order to gain insights into the mechanisms that underlie network growth and evolution. We recently discovered a relationship between graph theory and formal language theory that thinks like a context free grammar (CFG), but for graphs. The extracted hyperedge replacement grammar (HRG) contains the precise building blocks of the network as well as the instructions by which these building blocks ought to be pieced together to make predictions about the data. In a second project, we present work on how humans create networks of information and how we can leverage some of those networks pursuit of knowledge. At a time when information seekers first turn to digital sources for news and information, it is critical that we understand the role that social media plays in the creation of digital artifacts. This is especially true when information consumers also act as information producers and editors through their online activity. In order to better understand the effects that editorial ratings have on online human behavior, we report the results of a two large-scale experiments in social media.
Bio: Tim Weninger is an Assistant Professor at the University of Notre Dame where he directs the Data Science Group and is a member of the Interdisciplinary Center for Networks Science and Applications (ICENSA). His research interests are in data mining, machine learning and network science. The key application of his research is to identify how humans generate, curate and search for information in the pursuit of knowledge. He uses properties of these emergent networks to reason about the nature of relatedness, membership and other abstract and physical phenomena.