Network Science Pub Date : 2024-01-19 , DOI: 10.1017/nws.2023.22 Hanh T. D. Pham , Daniel K. Sewell
Most community detection methods focus on clustering actors with common features in a network. However, clustering edges offers a more intuitive way to understand the network structure in many real-life applications. Among the existing methods for network edge clustering, the majority are algorithmic, with the exception of the latent space edge clustering (LSEC) model proposed by Sewell (Journal of Computational and Graphical Statistics, 30(2), 390–405, 2021). LSEC was shown to have good performance in simulation and real-life data analysis, but fitting this model requires prior knowledge of the number of clusters and latent dimensions, which are often unknown to researchers. Within a Bayesian framework, we propose an extension to the LSEC model using a sparse finite mixture prior that supports automated selection of the number of clusters. We refer to our proposed approach as the automated LSEC or aLSEC. We develop a variational Bayes generalized expectation-maximization approach and a Hamiltonian Monte Carlo-within Gibbs algorithm for estimation. Our simulation study showed that aLSEC reduced run time by 10 to over 100 times compared to LSEC. Like LSEC, aLSEC maintains a computational cost that grows linearly with the number of actors in a network, making it scalable to large sparse networks. We developed the R package aLSEC which implements the proposed methodology.
中文翻译:
通过过度拟合的混合先验自动检测边缘簇
大多数社区检测方法侧重于对网络中具有共同特征的参与者进行聚类。然而,边缘聚类提供了一种更直观的方式来理解许多现实应用中的网络结构。现有的网络边缘聚类方法中,除了 Sewell 提出的潜在空间边缘聚类(LSEC)模型外,大多数都是算法方法(