Estimating network edge probabilities by neighbourhood smoothing

• 成果类型: Article
• 署名作者: Zhang, Yuan; Levina, Elizaveta; Zhu, Ji
• 署名单位: University System of Ohio; Ohio State University; University of Michigan System; University of Michigan
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asx042
• 发表日期: 2017
• 页码: 771783
• 关键词: Community Detection; Oracle Inequalities; models

摘要

The estimation of probabilities of network edges from the observed adjacency matrix has important applications to the prediction of missing links and to network denoising. It is usually addressed by estimating the graphon, a function that determines the matrix of edge probabilities, but this is ill-defined without strong assumptions on the network structure. Here we propose a novel computationally efficient method, based on neighbourhood smoothing, to estimate the expectation of the adjacency matrix directly, without making the structural assumptions that graphon estimation requires. The neighbourhood smoothing method requires little tuning, has a competitive mean squared error rate and outperforms many benchmark methods for link prediction in simulated and real networks.