  This note discusses a nonparametric approach to link regression aiming at
predicting a mean outcome at a link, i.e., a pair of nodes, based on currently
observed data comprising covariates at nodes and outcomes at links. The
variance decay rates of nonparametric link regression estimates are
demonstrated to depend on covariate designs; namely, whether the covariate
design is random or fixed. This covariate design-dependent nature of variance
is observed in nonparametric link regression but not in conventional
nonparametric regression.
