Data Science Lecture Series. Speaker: Blair Sullivan

Title: Improving Fairness of Information Access in Networks
Abstract: In social networks, node position is a form of social capital which enables faster and more reliable access to diverse information. Structural biases often arise from network formation and can lead to significant disparities in information access based on position. We discuss ways to quantify this social capital through the lens of information flow in the network, focusing on the setting where each node may be a source of distinct desirable information. We define several measures of access advantage, and consider the problem of improving equity by making interventions in the network, focusing on the case of edge augmentation. We describe several heuristic strategies for budgeted intervention, and present the results of an empirical evaluation on a corpus of real-world networks.
Bio: Blair D. Sullivan is a Professor in the School of Computing at the University of Utah. Prior to joining Utah, Dr. Sullivan was an Associate Professor at NC State University, and before that a Research Scientist at Oak Ridge National Laboratory. She received her Ph.D. in Mathematics from Princeton University in 2008 as a Department of Homeland Security Graduate Fellow, and B.S. degrees in Applied Mathematics and Computer Science from Georgia Tech in 2003. Sullivan’s research cross-cuts the fields of data-driven science, parameterized graph algorithms, network science, and algorithm engineering with a recent focus on problems arising in computational genomics. In 2014, Sullivan was named one of 14 Moore Investigators in Data-Driven Discovery. She currently serves on the Steering Committee for SODA, and was recently elected Chair of the SIAM SIAG on Applied & Computational Discrete Algorithms.