Psychology Department, Michigan State University, East Lansing, MI, United States of America.
PLoS One. 2022 May 31;17(5):e0269137. doi: 10.1371/journal.pone.0269137. eCollection 2022.
Networks are useful for representing phenomena in a broad range of domains. Although their ability to represent complexity can be a virtue, it is sometimes useful to focus on a simplified network that contains only the most important edges: the backbone. This paper introduces and demonstrates a substantially expanded version of the backbone package for R, which now provides methods for extracting backbones from weighted networks, weighted bipartite projections, and unweighted networks. For each type of network, fully replicable code is presented first for small toy examples, then for complete empirical examples using transportation, political, and social networks. The paper also demonstrates the implications of several issues of statistical inference that arise in backbone extraction. It concludes by briefly reviewing existing applications of backbone extraction using the backbone package, and future directions for research on network backbone extraction.
网络在广泛的领域中表示现象非常有用。虽然它们表示复杂性的能力可能是一种优点，但有时关注仅包含最重要边的简化网络是有用的：骨干。本文介绍并演示了 R 中骨干包的大幅扩展版本，该版本现在提供了从加权网络、加权二分图投影和无权重网络中提取骨干的方法。对于每种类型的网络，首先为小的玩具示例提供了完全可复制的代码，然后为使用运输、政治和社交网络的完整经验示例提供了代码。本文还演示了在骨干提取中出现的几个统计推断问题的影响。最后简要回顾了使用骨干包进行骨干提取的现有应用，并对网络骨干提取的研究方向进行了展望。
