Department of Physics and Astronomy, Northwestern University, Evanston, IL, 60208, USA.
Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, 60208, USA.
Nat Commun. 2023 May 24;14(1):2988. doi: 10.1038/s41467-023-38625-z.
Current computational methods for validating experimental network datasets compare overlap, i.e., shared links, with a reference network using a negative benchmark. However, this fails to quantify the level of agreement between the two networks. To address this, we propose a positive statistical benchmark to determine the maximum possible overlap between networks. Our approach can efficiently generate this benchmark in a maximum entropy framework and provides a way to assess whether the observed overlap is significantly different from the best-case scenario. We introduce a normalized overlap score, Normlap, to enhance comparisons between experimental networks. As an application, we compare molecular and functional networks, resulting in an agreement network of human as well as yeast network datasets. The Normlap score can improve the comparison between experimental networks by providing a computational alternative to network thresholding and validation.
目前，用于验证实验网络数据集的计算方法是使用负基准比较重叠，即共享链接，与参考网络。然而，这种方法未能量化两个网络之间的一致性程度。为了解决这个问题，我们提出了一种正统计基准来确定网络之间最大可能的重叠。我们的方法可以在最大熵框架中有效地生成这个基准，并提供了一种方法来评估观察到的重叠是否与最佳情况显著不同。我们引入了归一化重叠得分 Normlap 来增强实验网络之间的比较。作为一个应用，我们比较了分子和功能网络，得到了人类和酵母网络数据集的一致网络。Normlap 得分可以通过提供网络阈值和验证的计算替代方法来改善实验网络之间的比较。
