Network dependence testing via diffusion maps and distance-based correlations

• 成果类型: Article
• 署名作者: Lee, Youjin; Shen, Cencheng; Priebe, Carey E.; Vogelstein, Joshua T.
• 署名单位: University of Pennsylvania; University of Delaware; Johns Hopkins University; Johns Hopkins University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asz045
• 发表日期: 2019
• 页码: 857873
• 关键词: Optimization; selection; models; graphs

摘要

Deciphering the associations between network connectivity and nodal attributes is one of the core problems in network science. The dependency structure and high dimensionality of networks pose unique challenges to traditional dependency tests in terms of theoretical guarantees and empirical performance. We propose an approach to test network dependence via diffusion maps and distance-based correlations. We prove that the new method yields a consistent test statistic under mild distributional assumptions on the graph structure, and demonstrate that it is able to efficiently identify the most informative graph embedding with respect to the diffusion time. The methodology is illustrated on both simulated and real data.