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Activity Number: 35 - Special Session: Section on Nonparametric Statistics Student Paper Competition
Type: Contributed
Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #322746 View Presentation
Title: Network Cross-Validation by Edge Sampling
Author(s): Tianxi Li* and Liza Levina and Ji Zhu
Companies: and University of Michigan and University of Michigan
Keywords: network modeling ; cross-validaiton ; parameter tuning ; model selection

Many models and methods are now available for network analysis, but model selection and tuning remain challenging. Cross-validation is a useful general tool for these tasks in many settings, but is not directly applicable to networks since splitting network nodes into groups requires deleting edges and destroys some of the network structure. Here we propose a new network cross-validation strategy based on splitting edges rather than nodes, which avoids losing information and is applicable to a wide range of network problems. We provide a theoretical justification for our method in a general setting, and in particular show that the method has good asymptotic properties under the stochastic block model. Numerical results on both simulated and real networks show that our approach performs well for a number of model selection and parameter tuning tasks.

Authors who are presenting talks have a * after their name.

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