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

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 …

accuracy assumptions challenges comparison computation discovery false fdr hashing hypothesis inference math.st memory multiple network node rate registration sample sparsity stat.me stat.ml stat.th testing them