非负权网络的稀疏潜在位置模型
The Sparse Latent Position Model for nonnegative weighted networks
摘要：
本文介绍了一种分析具有非负边值的二部和一部网络的新方法。所提出的方法结合并适应了一些想法，从文献中的潜变量网络模型。由此产生的框架是一种新型的潜在位置模型，具有很大的灵活性，并能够捕捉到重要的功能，通常所观察到的网络，如稀疏性和重尾度分布。所提出的方法的一个重要优点是，在一个单一的算法框架中的数据自动推导出的潜在维度的数量。此外，该模型为每个潜在维度赋予权重，从而提供了对其相对重要性的度量。提出了一种快速变分贝叶斯算法来估计模型的参数。最后，所提出的方法的应用程序都说明了人工和真实的数据集，并与其他现有的程序进行比较。
英文摘要：
This paper introduces a new methodology to analyse bipartite and unipartite networks with nonnegative edge values. The proposed approach combines and adapts a number of ideas from the literature on latent variable network models. The resulting framework is a new type of latent position model which exhibits great flexibility, and is able to capture important features that are generally exhibited by observed networks, such as sparsity and heavy tailed degree distributions. A crucial advantage of the proposed method is that the number of latent dimensions is automatically deduced from the data in one single algorithmic framework. In addition, the model attaches a weight to each of the latent dimensions, hence providing a measure of their relative importance. A fast variational Bayesian algorithm is proposed to estimate the parameters of the model. Finally, applications of the proposed methodology are illustrated on both artificial and real datasets, and comparisons with other existing procedures are provided.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
