Most researchers have estimated the edge weights for relative importance networks using a well-established measure of general dominance for multiple regression. This approach has several desirable properties including edge weights that represent R² contributions, in-degree centralities that correspond to R² for each item when using other items as predictors, and strong replicability. We endorse the continued use of relative importance networks and believe they have a valuable role in network psychometrics. However, to improve their utility, we introduce a modified approach that uses best-subsets regression as a preceding step to select an appropriate subset of predictors for each item. The benefits of this modification include: (a) computation time savings that can enable larger relative importance networks to be estimated, (b) a principled approach to edge selection that can significantly improve specificity, (c) the provision of a signed network if desired, (d) the potential use of the best-subsets regression approach for estimating Gaussian graphical models, and (e) possible generalization to best-subsets logistic regression for Ising models. We describe, evaluate, and demonstrate the proposed approach and discuss its strengths and limitations.

中文翻译：

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一种拟合相对重要性网络的改进方法。

大多数研究人员已经使用成熟的多元回归一般优势度量来估计相对重要性网络的边缘权重。这种方法有几个理想的属性，包括代表 R² 贡献的边缘权重，对应于 R 的度中心性² 对每个item使用其他item作为预测器时，可复制性强。我们支持继续使用相对重要性网络，并相信它们在网络心理测量学中具有重要作用。然而，为了提高它们的效用，我们引入了一种改进的方法，该方法使用最佳子集回归作为前面的步骤，为每个项目选择适当的预测变量子集。这种修改的好处包括：（a）计算时间节省，可以估计更大的相对重要性网络，（b）边缘选择的原则方法，可以显着提高特异性，（c）如果需要，提供签名网络, (d) 最佳子集回归方法在估计高斯图形模型中的潜在用途，以及 (e) 可能推广到 Ising 模型的最佳子集逻辑回归。