Higher-order accurate two-sample network inference and network hashing

• URL: http://arxiv.org/abs/2208.07573v3
• Date: Fri, 2 Feb 2024 15:04:36 GMT
• Title: Higher-order accurate two-sample network inference and network hashing
• Authors: Meijia Shao, Dong Xia, Yuan Zhang, Qiong Wu and Shuo Chen
• Abstract summary: Two-sample hypothesis testing for network comparison presents many significant challenges. We develop a comprehensive toolbox featuring a novel main method and its variants. Our method outperforms existing tools in speed and accuracy, and it is proved power-optimal.
• Score: 13.984114642035692
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Two-sample hypothesis testing for network comparison presents many significant challenges, including: leveraging repeated network observations and known node registration, but without requiring them to operate; relaxing strong structural assumptions; achieving finite-sample higher-order accuracy; handling different network sizes and sparsity levels; fast computation and memory parsimony; controlling false discovery rate (FDR) in multiple testing; and theoretical understandings, particularly regarding finite-sample accuracy and minimax optimality. In this paper, we develop a comprehensive toolbox, featuring a novel main method and its variants, all accompanied by strong theoretical guarantees, to address these challenges. Our method outperforms existing tools in speed and accuracy, and it is proved power-optimal. Our algorithms are user-friendly and versatile in handling various data structures (single or repeated network observations; known or unknown node registration). We also develop an innovative framework for offline hashing and fast querying as a very useful tool for large network databases. We showcase the effectiveness of our method through comprehensive simulations and applications to two real-world datasets, which revealed intriguing new structures.

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