Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities
提出一种用于稀疏网络且具有重叠社区结构的统计模型，基于可交换点过程，每个节点被赋予一个表示其社区隶属关系的向量，并开发了高效模拟和可扩展后验推断方法。
Summary We propose a novel statistical model for sparse networks with overlapping community structure. The model is based on representing the graph as an exchangeable point process and naturally generalizes existing probabilistic models with overlapping block structure to the sparse regime. Our construction builds on vectors of completely random measures and has interpretable parameters, each node being assigned a vector representing its levels of affiliation to some latent communities. We develop methods for efficient simulation of this class of random graphs and for scalable posterior inference. We show that the approach proposed can recover interpretable structure of real world networks and can handle graphs with thousands of nodes and tens of thousands of edges.