强者是多么强的?强者是多么强的? 解释网络边缘权重的挑战
1Psychology Department, Michigan State University, East Lansing, MI, United States of America.
PloS one
|October 3, 2024
概括
分析加权网络揭示了边缘权重解释的模糊性. 骨干模型可以识别强的边缘,但在进一步分析中不应该直接使用它们的原始权重.
科学领域:
- 网络科学 网络科学
- 图形理论 图形理论
- 数据分析 数据分析
背景情况:
- 权重网络提供了丰富的信息,但由于模两可的边缘权重解释,造成了分析挑战.
- 边缘权重的含义取决于上下文,甚至在同一网络内变化.
研究的目的:
- 调查加权网络中边缘权重解释的模两可.
- 评估骨干模型在区分强弱边缘方面的有效性.
主要方法:
- 一个由110个加权网络组成的集体的分析.
- 应用骨干模型来区分边缘强度.
主要成果:
- 虽然强边缘通常比弱边缘具有更高的重量,但很大一部分边缘重量提供了不清楚的信息.
- 在没有适当模型的情况下解释边缘权重时存在相当大的模两可.
结论:
- 推使用骨干模型来可靠地识别加权网络中的强边缘.
- 鉴定出强边缘的原始重量不应直接解释或用于后续分析,以避免模两可.
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