Network cross-validation by edge sampling
Network cross-validation by edge sampling
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DOI：
10.1093/biomet/asaa006
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发表时间：
2020-06-01
期刊：
影响因子：
2.7
通讯作者：
Zhu, Ji
中科院分区：
文献类型：
--
作者：
Li, Tianxi;Levina, Elizaveta;Zhu, Ji
虽然现在有许多统计模型和方法可用于网络分析，但网络数据的恢复仍然是一个具有挑战性的问题。交叉验证是模型选择和参数调整的有用的通用工具，但它不直接适用于网络，因为将网络节点分成组需要删除边缘并破坏一些网络结构。在本文中，我们提出了一个新的网络rescue策略，分裂节点对，而不是节点的基础上，适用于交叉验证的广泛的网络模型选择任务。我们提供了理论上的理由，我们的方法在一般的设置和例子，该方法可以用于特定的网络模型选择和参数调整任务。模拟网络和统计学家的引文网络上的数值结果表明，所提出的交叉验证方法的模型选择。
While many statistical models and methods are now available for network analysis, resampling of network data remains a challenging problem. Cross-validation is a useful general tool for model selection and parameter tuning, but it is not directly applicable to networks since splitting network nodes into groups requires deleting edges and destroys some of the network structure. In this paper we propose a new network resampling strategy, based on splitting node pairs rather than nodes, that is applicable to cross-validation for a wide range of network model selection tasks. We provide theoretical justification for our method in a general setting and examples of how the method can be used in specific network model selection and parameter tuning tasks. Numerical results on simulated networks and on a statisticians' citation network show that the proposed cross-validation approach works well for model selection.