'Statistics 101' for Network Data Objects

Eric Kolaczyk, Boston University

Friday, January 17, 2020 - 3:30pm

It is becoming increasingly common to see large collections of network data objects -- that is, data sets in which a network is viewed as a fundamental unit of observation. As a result, there is a pressing need to develop network-based analogues of even many of the most basic techniques already standard for scalar and vector data. At the same time, principled extensions of familiar techniques to this context are nontrivial, given that networks are inherently non-Euclidean. I will present a number of results extending the notion of asymptotic inference for means to the contexts of various types of networks, i.e., both labeled and unlabeled, and either single- or multi-layer. These results rely on a combination of tools from geometry, probability theory, and statistical shape analysis. I will illustrate drawing from various applications in bioinformatics, computational neuroscience, and social network analysis under privacy.  If time permits, I will also preview some of our current work on extension of Gaussian process models for classification, anomaly detection, and survival analysis with network-data inputs.

Seminars generally take place in 116 Old Chemistry Building on Fridays from 3:30 - 4:30 pm. For additional information contact: karen.whitesell@duke.edu or phone 919-684-8029. Sorry, but we do not have reprints available. Please feel free to contact the authors by email for follow-up information, articles, etc. Reception following seminar in 203B Old Chemistry.

Old Chemistry 116

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