Community detection methods have been extensively studied to recover communities structures in network data. While many models and methods focus on binary data, real-world networks also present the strength of connections, which could be considered in the network analysis. We propose a probabilistic model for generating weighted networks that allows us to control network sparsity and incorporates degree corrections for each node. We propose a community detection method based on the Variational Expectation-Maximization (VEM) algorithm. We show that the proposed method works well in practice for simulated networks. We analyze the Brazilian airport network to compare the community structures before and during the COVID-19 pandemic.

翻译：社区检测方法已被广泛研究以恢复网络数据中的社区结构。尽管许多模型和方法专注于二元数据，现实世界网络还呈现连接强度，这在网络分析中应予以考虑。我们提出了一种生成加权网络的概率模型，该模型允许我们控制网络稀疏性，并为每个节点纳入度校正。我们提出了一种基于变分期望最大化（VEM）算法的社区检测方法。我们证明所提方法在模拟网络中实际表现良好。我们分析了巴西机场网络，以比较COVID-19疫情前后社区结构的变化。