Snowboot: Bootstrap Methods for Network Inference
Snowboot: Bootstrap Methods for Network Inference
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DOI：
10.32614/rj-2018-056
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发表时间：
2019-02
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影响因子：
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通讯作者：
Yuzhou Chen;Y. Gel;V. Lyubchich;Kusha Nezafati
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文献类型：
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作者：
Yuzhou Chen;Y. Gel;V. Lyubchich;Kusha Nezafati
复杂网络被用来描述各种不同的社会系统和自然现象，从电网到客户细分到人类大脑连接体。参数模型规范和验证的挑战激发了对复杂网络推理的更多数据驱动和灵活的非参数方法的探索。在本文中，我们讨论了随机网络上两个引导过程的方法和R实现，即Thompson等人（2016）和Gel等人（2017）的补丁引导和Snijders和Borgatti（1999）的顶点引导。据我们所知，新的R包snowboot是第一个在R中实现顶点和补丁引导推理的网络。我们的新包附带了详细的用户手册，并且与流行的R包在网络研究图上兼容。我们通过大量的仿真研究来评估拼接自举和顶点自举，并说明它们在分析现实世界网络中的应用。
Complex networks are used to describe a broad range of disparate social systems and natural phenomena, from power grids to customer segmentation to human brain connectome. Challenges of parametric model specification and validation inspire a search for more data-driven and flexible nonparametric approaches for inference of complex networks. In this paper we discuss methodology and R implementation of two bootstrap procedures on random networks, that is, patchwork bootstrap of Thompson et al. (2016) and Gel et al. (2017) and vertex bootstrap of Snijders and Borgatti (1999). To our knowledge, the new R package snowboot is the first implementation of the vertex and patchwork bootstrap inference on networks in R. Our new package is accompanied with a detailed user's manual, and is compatible with the popular R package on network studies igraph. We evaluate the patchwork bootstrap and vertex bootstrap with extensive simulation studies and illustrate their utility in application to analysis of real world networks.