Two-Sample Testing for Inhomogeneous Random Graphs in Non-Integral Lr Norms

Testing whether two populations of networks share the same edge probabilities is a basic problem in network inference. How hard it is depends on the norm used to measure the difference. For the inhomogeneous ErdHos--R'enyi (IER) model, the optimal sample complexity is known for every integer Lr norm and for 1≤ r<2. For non-integral r>2, however, the known upper and lower bounds do not match, and the lower bound was conjectured to be tight. We study this gap for two-sample testing on aligned vertices. We propose a test that runs two published statistics, of orders 2 and lceil rrceil, on the same data and rejects if either one rejects. Its thresholds come from H"older interpolation, so that both statistics have the same sample cost. We prove that this test attains the conjectured rate. Combined with earlier results, this shows that for every fixed r≥1 the minimax sample complexity is of order n^max4/r-1,,2/r/ε2, even when the separation changes with n. In simulations with n between 32 and 256, the number of graphs needed for 80% power at level 0.05 grows with n at a rate consistent with the theory. For r=2.5, for example, the fitted exponent is 0.78, against the theoretical value 0.8. Interestingly, the two statistics split the work as the interpolation argument suggests: the higher-order statistic is more powerful when only a few edges change, and the L2 statistic when many edges change.