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Theoretical and Empirical Guides for Selecting Algorithms on Complex Networks

07 Oct
Friday, 10/07/2016 12:00pm to 2:00pm
Computer Science Building, Room 150/151
CSSI Lunch

Lunch will be provided, beginning at 12:00 pm
Talk begins at 12:30 pm

"Theoretical and Empirical Guides for Selecting Algorithms on Complex Networks

Abstract:  In this talk, I will discuss two problems on complex networks. (1) Measuring tie-strength: Given a set of people and a set of events attended by them, how should we measure connectedness or tie strength between each pair of persons? The underlying assumption is that attendance at mutual events produces an implicit social network between people. I will describe an axiomatic solution to this problem. (2) Network similarity: Given two networks (without known node-correspondences), how should we measure similarity between them? This problem occurs frequently in many real-world applications such as transfer learning, re-identification, and change detection. I will present an empirical guide on how to select a network-similarity method.

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 120 invited presentations. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2010, she received an Outstanding Mentor Award from the US DOE Office of Science. For more details, visit http://eliassi.org.