Psychology Department, Michigan State University, East Lansing, MI, United States of America.
PLoS One. 2024 Oct 3;19(10):e0311614. doi: 10.1371/journal.pone.0311614. eCollection 2024.
Weighted networks are information-rich and highly-flexible, but they can be difficult to analyze because the interpretation of edges weights is often ambiguous. Specifically, the meaning of a given edge's weight is locally contingent, so that a given weight may be strong for one dyad, but weak for other dyad, even in the same network. I use backbone models to distinguish strong and weak edges in a corpus of 110 weighted networks, and used the results to examine the magnitude of this ambiguity. Although strong edges have larger weights than weak edges on average, a large fraction of edges' weights provide ambiguous information about whether it is strong or weak. Based on these results, I recommend that strong edges should be identified by applying an appropriate backbone model, and that once strong edges have been identified using a backbone model, their original weights should not be directly interpreted or used in subsequent analysis.
加权网络信息丰富且高度灵活，但由于边缘权重的解释往往不明确，因此很难进行分析。具体来说，给定边权重的含义在局部上是偶然的，因此对于同一网络中的一个对子，给定的权重可能很强，但对于另一个对子可能很弱。我使用骨干模型来区分 110 个加权网络中的强边和弱边，并使用结果来检验这种模糊性的程度。虽然强边的权重平均大于弱边，但很大一部分边的权重提供了关于其强弱的模糊信息。基于这些结果，我建议通过应用适当的骨干模型来识别强边，并且一旦使用骨干模型识别出强边，就不应直接解释或在后续分析中使用它们的原始权重。
