Signal processing on graphs is a new emerging field that processing high-dimensional data by spreading samples on networks or graphs. The new introduced definition of graph Fourier transform shows its importance in establishing the theory of frequency analysis or computational harmonic analysis on graph signal processing. We introduce the definition of redundant graph Fourier transform, which is defined via a Parseval frame transform generated from an extended Laplacian of a given graph. The flexibility and sparsity of the redundant graph Fourier transform are important properties that will be useful in signal processing. In certain applications and by selections of the extended Laplacian, redundant Fourier transform performs better than graph Fourier transform.