  We propose a supervised learning approach for predicting an underlying graph
from a set of graph signals. Our approach is based on linear regression. In the
linear regression model, we predict edge-weights of a graph as the output,
given a set of signal values on nodes of the graph as the input. We solve for
the optimal regression coefficients using a relevant optimization problem that
is convex and uses a graph-Laplacian based regularization. The regularization
helps to promote a specific graph spectral profile of the graph signals.
Simulation experiments demonstrate that our approach predicts well even in
presence of outliers in input data.
