Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models
Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models
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DOI：
10.1016/j.csda.2020.107051
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发表时间：
2020-12-01
影响因子：
1.8
通讯作者：
Ludkin, Matthew
中科院分区：
文献类型：
--
作者：
Ludkin, Matthew
随机块模型（SBM）是一个流行的模型，用于捕获网络中的社区结构和相互作用。具有非布尔边权重的网络数据变得越来越普遍;然而，现有的分析方法将此类数据转换为二进制表示以应用SBM，从而导致信息丢失。被认为是一个概括的SBM，它允许边缘权重建模在其记录的状态。提出了一种有效的可逆跳马尔可夫链蒙特卡罗采样器，用于估计这种广义SBM的参数和块数。该方法允许边缘权重的非共轭分布，这使得建模比当前方法更灵活，如在合成数据、大脑活动网络和电子邮件通信网络上所示。(C)2020由爱思唯尔公司出版
The stochastic block model (SBM) is a popular model for capturing community structure and interaction within a network. Network data with non-Boolean edge weights is becoming commonplace; however, existing analysis methods convert such data to a binary representation to apply the SBM, leading to a loss of information. A generalisation of the SBM is considered, which allows edge weights to be modelled in their recorded state. An effective reversible jump Markov chain Monte Carlo sampler is proposed for estimating the parameters and the number of blocks for this generalised SBM. The methodology permits non-conjugate distributions for edge weights, which enable more flexible modelling than current methods as illustrated on synthetic data, a network of brain activity and an email communication network. (C) 2020 Published by Elsevier B.V.