A0295
Title: Higher-order accurate two-sample network inference and network hashing
Authors: Yuan Zhang - The Ohio State University (United States) [presenting]
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. A comprehensive toolbox is developed, featuring a novel main method and its variants, all accompanied by strong theoretical guarantees, to address these challenges. The method outperforms existing tools in speed and accuracy, and it is proven power-optimal. Algorithms are user-friendly and versatile in handling various data structures (single or repeated network observations; known or unknown node registration). An innovative framework has also been developed for offline hashing and fast querying, which is a very useful tool for large network databases. The effectiveness of the method is showcased through comprehensive simulations and applications to two real-world datasets, which revealed intriguing new structures.
