Perspective on Link Prediction

Link prediction is the task of predicting relationships in a network. As interest in network science grew, so did the realization of the broad applicability of general link prediction — from security to collaboration to marketing to information flow to biology and medicine. Such broad applicability also brings forth a number of challenges to consider, including generality of methodologies, modes of evaluation, and scalability. In this talk, I shall offer a perspective on link prediction for both homogeneous and heterogeneous information networks, and present applications in social networks and biology/medicine.
Nitesh Chawla is an Associate Professor in the Department of Computer
Science and Engineering, Director of Data Inference Analysis and
Learning Lab (DIAL), and co-Director of the Interdisciplinary Center for
Network Science and Applications (iCeNSA). His research is focused on machine
learning, data mining, and network science with interdisciplinary
connections to climate data sciences, healthcare informatics, and social
networks.
He is the recipient of multiple awards for research and teaching
innovation including outstanding dissertation award, outstanding
undergraduate Teacher in 2008 and 2011, National Academy of
Engineers New Faculty Fellowship, and number of best paper awards and
nominations. His research is currently supported by National Science Foundation, the Department of Energy, the Army Research
Labs, and a number of Industry Sponsors. He is the chair of the IEEE CIS Data
Mining Technical Committee.