Sampling and reconstruction of sparse signals on circulant graphs–an introduction to graph-FRI

Madeleine Kotzagiannidis, Pier Luigi Dragotti

Research output: Contribution to journalArticlepeer-review


With the objective of employing graphs toward a more generalized theory of signal processing, we present a novel sampling framework for (wavelet-)sparse signals defined on circulant graphs which extends basic properties of Finite Rate of Innovation (FRI) theory to the graph domain, and can be applied to arbitrary graphs via suitable approximation schemes. At its core, the introduced Graph-FRI-framework states that any K-sparse signal on the vertices of a circulant graph can be perfectly reconstructed from its dimensionality-reduced representation in the graph spectral domain, the Graph Fourier Transform (GFT), of minimum size 2K. By leveraging the recently developed theory of e-splines and e-spline wavelets on graphs, one can decompose this graph spectral transformation into the multiresolution low-pass filtering operation with a graph e-spline filter, with subsequent transformation to the spectral graph domain; this allows to infer a distinct sampling pattern, and, ultimately, the structure of an associated coarsened graph, which preserves essential properties of the original, including circularity and, where applicable, the graph generating set.
Original languageEnglish
JournalApplied and Computational Harmonic Analysis
Early online date18 Oct 2017
Publication statusE-pub ahead of print - 18 Oct 2017


  • Graph signal processing
  • Sampling on graphs
  • Sparse sampling
  • Graph wavelet
  • Finite rate of innovation


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