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University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Matrix completion in network analysis

## Matrix completion in network analysisAdd to your list(s) Download to your calendar using vCal - Elizaveta Levina (University of Michigan)
- Wednesday 27 June 2018, 09:45-10:30
- Seminar Room 1, Newton Institute.
If you have a question about this talk, please contact info@newton.ac.uk. STSW04 - Future challenges in statistical scalability Matrix completion is an active area of research in itself, and a natural tool to apply to network data, since many real networks are observed incompletely and/or with noise. However, developing matrix completion algorithms for networks requires taking into account the network structure. This talk will discuss three examples of matrix completion used for network tasks. First, we discuss the use of matrix completion for cross-validation on network data, a long-standing problem in network analysis. Two other examples focus on reconstructing incompletely observed networks, with structured missingness resulting from network sampling mechanisms. One scenario we consider is egocentric sampling, where a set of nodes is selected first and then their connections to the entire network are observed. Another scenario focuses on data from surveys, where people are asked to name a given number of friends. We show that matrix completion, when used appropriately, can generally be very helpful in solving network problems. This talk is part of the Isaac Newton Institute Seminar Series series. ## This talk is included in these lists:- All CMS events
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