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  • 统计与管理学院2017年学术报告第25

     

    【主  题】Network Cross-Validation by Edge Sampling

    【报告人】朱冀, 教授

    University of Michigan

    【时  间】 2017年05月12日(星期五)16:00-17:00

    【地  点】 上海财经大学统计与管理学院大楼1208室

    【摘  要】 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.  This is joint work with Tianxi Li and Elizaveta Levina.

    【邀请人】 陈艳