The aim of this article is to develop a Bayesian random graph mixture model (RGMM) to detect the latent class network (LCN) structure of brain connectivity networks and estimate the parameters governing this structure. The use of conjugate priors for unknown parameters leads to efficient estimation, and a well-known nonidentifiability issue is avoided by a particular parameterization of the stochastic block model (SBM). Posterior computation proceeds via an efficient Markov Chain Monte Carlo algorithm. Simulations demonstrate that LCN outperforms several other competing methods for community detection in weighted networks, and we apply our RGMM to estimate the latent community structures in the functional resting brain networks of 185 subjects from the ADHD-200 sample. We find overlap in the estimated community structure across subjects, but also heterogeneity even within a given diagnosis group.

中文翻译：

---

LCN：功能性大脑网络中社区检测的随机图混合模型


本文的目的是开发贝叶斯随机图混合模型（RGMM）来检测大脑连接网络的潜在类网络（LCN）结构并估计控制该结构的参数。对未知参数使用共轭先验可以实现有效的估计，并且通过随机块模型 (SBM) 的特定参数化避免了众所周知的不可识别性问题。后验计算通过高效的马尔可夫链蒙特卡罗算法进行。模拟表明，LCN 在加权网络中的社区检测方面优于其他几种竞争方法，并且我们应用 RGMM 来估计 ADHD-200 样本中 185 名受试者的功能性静息大脑网络中的潜在社区结构。我们发现受试者之间估计的社区结构存在重叠，但即使在给定的诊断组内也存在异质性。