  This paper discusses a special kind of a simple yet possibly powerful
algorithm, called single-kernel Gradraker (SKG), which is an adaptive learning
method predicting unknown nodal values in a network using known nodal values
and the network structure. We aim to find out how to configure the special kind
of the model in applying the algorithm. To be more specific, we focus on SKG
with a Gaussian kernel and specify how to find a suitable variance for the
kernel. To do so, we introduce two variables with which we are able to set up
requirements on the variance of the Gaussian kernel to achieve (near-) optimal
performance and can better understand how SKG works. Our contribution is that
we introduce two variables as analysis tools, illustrate how predictions will
be affected under different Gaussian kernels, and provide an algorithm finding
a suitable Gaussian kernel for SKG with knowledge about the training network.
Simulation results on real datasets are provided.
