Clustering spatial networks through latent mixture models
提出一种贝叶斯模型聚类方法，直接考虑地域单元间的网络关系及其地理位置，用于设计意大利大区中市镇与省之间的中间行政结构，基于通勤流数据。
Abstract We consider a Bayesian model-based clustering technique that directly accounts for network relations between territorial units and their position in a geographical space. This proposal is motivated by a practical problem: to design administrative structures that are intermediate between the municipality and the province within an Italian region based on the existence of a relatively (to population) high commuting flow. In our social network model, the commuting flows are explained by the distances between the municipalities, i.e., the nodes, in a 3-dimensional space, where the 2 actual geographical coordinates and the third latent variable are modelled through a Gaussian mixture.