Department of Mathematical Sciences and Centre for Networks and Collective Behaviour, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom and ISI Foundation, Via Alassio 11/c, 10126 Torino, Italy.
Phys Rev E. 2018 Jan;97(1-1):012306. doi: 10.1103/PhysRevE.97.012306.
We present a Bayesian formulation of weighted stochastic block models that can be used to infer the large-scale modular structure of weighted networks, including their hierarchical organization. Our method is nonparametric, and thus does not require the prior knowledge of the number of groups or other dimensions of the model, which are instead inferred from data. We give a comprehensive treatment of different kinds of edge weights (i.e., continuous or discrete, signed or unsigned, bounded or unbounded), as well as arbitrary weight transformations, and describe an unsupervised model selection approach to choose the best network description. We illustrate the application of our method to a variety of empirical weighted networks, such as global migrations, voting patterns in congress, and neural connections in the human brain.
我们提出了一种加权随机块模型的贝叶斯公式，可以用于推断加权网络的大规模模块结构，包括它们的层次组织。我们的方法是无参数的，因此不需要先验知识的群体数量或模型的其他维度，而是从数据中推断出来。我们对不同类型的边权重（即连续或离散、有符号或无符号、有界或无界）以及任意权重转换进行了全面的处理，并描述了一种无监督的模型选择方法来选择最佳的网络描述。我们将我们的方法应用于各种经验加权网络，如全球移民、国会投票模式和人类大脑中的神经连接。
