Location: Rm 2018, VTCRC 1880 Pratt Drive Blacksburg, VA
Title: The reasonable effectiveness of roles in networks
Abstract: Given a graph, how can we automatically discover roles (or functions) of nodes? Roles compactly represent structural behaviors of nodes and generalize across various graphs. Examples of roles include "clique-members," "periphery-nodes," "bridges," etc. Are there good features that we can extract for nodes that indicate role-membership? How are roles diffevent from communities and from equivalences (from sociology)? What are the applications in which these discovered roles can be effectively used? In this talk, we address these questions, provide unsupervised and supervised algorithms for role discovery, and discuss why roles are so effective in many applications from transfer learning to re-identification to anomaly detection to mining time-evolving networks and multi-relational graphs.
Short Bio: Tina Eliassi-Rad is an Associate Professor of Computer Science at Rutgers University. Before joining academia, she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her current research lays at the intersection of graph mining, network science, and computational social science. Within data mining and machine learning, Tina's research has been applied to the World-Wide Web, text corpora, large-scale scientific simulation data, complex networks, fraud detection, and cyber situational awareness. She has published over 60 peer-reviewed papers (including a best paper runner-up award at ICDM'09 and a best interdisciplinary paper award at CIKM'12); and has given over 100 invited presentations. Tina is an action editor for the Data Mining and Knowledge Discovery Journal and a member of the editorial board for the Springer Encyclopedia of Machine Learning and Data Mining. In 2010, she received an Outstanding Mentor Award from the US DOE Office of Science. For more details, visit http://eliassi.org.