Several models for producing scale-free networks have been suggested; most of them are based on the preferential attachment approach. In this article, we suggest a new approach for generating scale-free networks with an alternative source of the power-law degree distribution. The model derives from matrix factorization methods and geographical threshold models that were recently proven to show good results in generating scale-free networks. We associate each node with a vector having latent features distributed over a unit sphere and with a weight variable sampled from a Pareto distribution. We join two nodes by an edge if they are spatially close and/or have large weights. The network produced by this approach is scale free and has a power-law degree distribution with an exponent of 2. In addition, we propose an extension of the model that allows us to generate directed networks with tunable power-law exponents.

中文翻译：

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无标度网络生成的分解阈值模型。

已经提出了几种产生无标度网络的模型；其中大部分是基于优先依附方法。在本文中，我们提出了一种新方法来生成具有幂律度分布的替代来源的无标度网络。该模型源自最近被证明在生成无标度网络方面显示出良好结果的矩阵分解方法和地理阈值模型。我们将每个节点与一个向量相关联，该向量具有分布在单位球面上的潜在特征，并与从帕累托分布中采样的权重变量相关联。如果它们在空间上接近和/或具有较大的权重，我们通过一条边连接两个节点。这种方法产生的网络是无标度的，并且具有指数为 2 的幂律度分布。此外，