  In this paper, we propose a new regression-based algorithm to compute Graph
Fourier Transform (GFT). Our algorithm allows different regularizations to be
included when computing the GFT analysis components, so that the resulting
components can be tuned for a specific task. We propose using the lasso penalty
in our proposed framework to obtain analysis components with sparse loadings.
We show that the components from this proposed {\em sparse GFT} can identify
and select correlated signal sources into sub-graphs, and perform frequency
analysis {\em locally} within these sub-graphs of correlated sources. Using
real network traffic datasets, we demonstrate that sparse GFT can achieve
outstanding performance in an anomaly detection task.
