Network cross-validation by edge sampling
报 告 人:: 朱冀
报告地点:: 数学与统计学院4楼报告厅
报告时间:: 2017年12月04日星期一15:30-16:30

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 simulated networks show that our approach performs well for a number of model selection and parameter tuning tasks. We also analyze a citation network of statisticians, with meaningful research communities emerging from the analysis. This is joint work with Tianxi Li and Elizaveta Levina.

发 布 人:科研助理 发布时间: 2017-12-04