  How can one determine whether a community-level treatment, such as the
introduction of a social program or trade shock, alters agents' incentives to
form links in a network? This paper proposes analogues of a two-sample
Kolmogorov-Smirnov test, widely used in the literature to test the null
hypothesis of "no treatment effects", for network data. It first specifies a
testing problem in which the null hypothesis is that two networks are drawn
from the same random graph model. It then describes two randomization tests
based on the magnitude of the difference between the networks' adjacency
matrices as measured by the $2\to2$ and $\infty\to1$ operator norms. Power
properties of the tests are examined analytically, in simulation, and through
two real-world applications. A key finding is that the test based on the
$\infty\to1$ norm can be substantially more powerful than that based on the
$2\to2$ norm for the kinds of sparse and degree-heterogeneous networks common
in economics.
