Model-Based Clustering of Nonparametric Weighted Networks with Application to Water Pollution Analysis.

ABSTRACT: Water pollution is a major global environmental problem, and it poses a great environmental risk to public health and biological diversity. This work is motivated by assessing the potential environmental threat of coal mining through increased sulfate concentrations in river networks, which do not belong to any simple parametric distribution. However, existing network models mainly focus on binary or discrete networks and weighted networks with known parametric weight distributions. We propose a principled nonparametric weighted network model based on exponential-family random graph models and local likelihood estimation, and study its model-based clustering with application to large-scale water pollution network analysis. We do not require any parametric distribution assumption on network

SUBMITTER: Agarwal A 

PROVIDER: S-EPMC7953862 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature