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 an application to analysis of real world networks.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-056.zip
snowboot, bootnet, sna, graphics, igraph, parallel, Rcpp, Rdpack, stats, VGAM
Optimization, SocialSciences, Bayesian, Distributions, Econometrics, Environmetrics, ExtremeValue, gR, Graphics, HighPerformanceComputing, Multivariate, NumericalMathematics, Psychometrics, Spatial, Survival
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For attribution, please cite this work as
Chen, et al., "The R Journal: Snowboot: Bootstrap Methods for Network Inference", The R Journal, 2018
BibTeX citation
@article{RJ-2018-056,
author = {Chen, Yuzhou and Gel, Yulia R. and Lyubchich, Vyacheslav and Nezafati, Kusha},
title = {The R Journal: Snowboot: Bootstrap Methods for Network Inference},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-056},
doi = {10.32614/RJ-2018-056},
volume = {10},
issue = {2},
issn = {2073-4859},
pages = {95-113}
}