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Abstract Details

Activity Number: 391
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract - #305594
Title: Link Prediction for Partially Observed Networks
Author(s): Yunpeng Zhao*+ and Liza Levina and Ji Zhu
Companies: University of Michigan and University of Michigan and University of Michigan
Address: , , ,
Keywords: link prediction ; supervised learning

A fundamental problem in network analysis is link prediction for partially observed networks. One difficulty of applying existing supervised learning approaches to link prediction is a lack of negative examples, i.e., there is often no certain information that no link exists between a pair. To address this situation, we present a new method, which treats all observed links as true positives and others as missing data. The missing rate cannot be estimated in this setup, but a relative ranking of probabilities of potential links can be. Our method provides a ranked list of pairs ordered by probability of link, which is sufficient in many applications. To obtain these rankings, we utilize information on node covariates as well as network topology. The method performs well on both simulated and real-world networks.

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