Higher-order accurate two-sample network inference and network hashing. (arXiv:2208.07573v1 [stat.ME])

Two-sample hypothesis testing for comparing two networks is an important yet
difficult problem. Major challenges include: potentially different sizes and
sparsity levels; non-repeated observations of adjacency matrices; computational
scalability; and theoretical investigations, especially on finite-sample
accuracy and minimax optimality. In this article, we propose the first provably
higher-order accurate two-sample inference method by comparing network moments.
Our method extends the classical two-sample t-test to the network setting. We
make weak modeling assumptions and can effectively handle networks of different
sizes …

arxiv hashing inference network