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.

中文翻译：

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具有未知块数和非共轭边缘模型的广义随机块模型的推断

随机块模型 (SBM) 是一种流行的模型，用于捕获网络内的社区结构和交互。具有非布尔边缘权重的网络数据正变得司空见惯；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，导致信息丢失。考虑了 SBM 的泛化，它允许在记录状态下对边缘权重进行建模。提出了一种有效的可逆跳跃马尔可夫链蒙特卡罗采样器来估计这个广义 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，如合成数据、大脑活动网络和电子邮件通信网络所示。