Exchangeable random measures for sparse and modular graphs with overlapping communities
Exchangeable random measures for sparse and modular graphs with overlapping communities
复制标题
具有重叠社区的稀疏和模块化图的可交换随机测量
DOI：
10.1111/rssb.12363
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
F. Caron
中科院分区：
文献类型：
--
作者：
A. Todeschini;F. Caron
我们提出了一种新的统计模型，稀疏网络重叠的社区结构。该模型的基础上表示的图形作为一个可交换的点过程，自然地概括了现有的概率模型与重叠块结构的稀疏制度。我们的构造建立在完全随机测量的向量上，并且具有可解释的参数，每个节点被分配一个向量，表示其与一些潜在社区的联系程度。我们开发了这类随机图的有效模拟和可扩展的后验推理的方法。我们表明，所提出的方法可以恢复真实的世界网络的可解释的结构，并可以处理具有数千个节点和数万条边的图。
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.