Skip to main content
Log in

Graph Signal Sampling and Interpolation Based on Clusters and Averages

  • Published:
Journal of Fourier Analysis and Applications Aims and scope Submit manuscript

Abstract

We consider a disjoint cover (partition) of an undirected weighted finite or infinite graph G by J connected subgraphs (clusters) \(\{S_{j}\}_{j\in J}\) and select functions \(\psi _{j}\) on each of the clusters. For a given signal f on G the set of its weighted average values samples is defined via inner products \(\{\langle f, \psi _{j}\rangle \}_{j\in J}\). The main results of the paper are based on Poincare-type inequalities that we introduce and prove. These inequalities provide an estimate of the norm of the signal f on the entire graph G from sets of samples of f and its local gradient on each of the subgraphs. This allows us to establish discrete Plancherel-Polya-type inequalities (or Marcinkiewicz-Zigmund-type or frame inequalities) for signals whose gradients satisfy a Bernstein-type inequality. These results enable the development of a sampling theory for signals on undirected weighted finite or infinite graphs. For reconstruction of the signals from their samples an interpolation theory by weighted average variational splines is developed. Here by a weighted average variational spline we understand a minimizer of a discrete Sobolev norm which takes on the prescribed weighted average values on a set of clusters (in particular, just values on a subset of vertices). Although our approach is applicable to general graphs it’s especially well suited for finite and infinite graphs with multiple clusters. Such graphs are known as community graphs and they find many important applications in materials science, engineering, computer science, economics, biology, and social studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Anis, A., Gadde, A., Ortega, A.: Towards a sampling theorem for signals on arbitrary graphs. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 3864–3868 (2014)

  2. Chen, S., Varma, R., Sandryhaila, A., Kovacevich, J.: Discrete signal processing on graphs: sampling theory. IEEE Trans. Signal Process. 63(24), 6510–6523 (2015)

    Article  MathSciNet  Google Scholar 

  3. Cheng, C., Jiang, Y., Sun, Q.: Spatially distributed sampling and reconstruction. Appl. Comput. Harmon. Anal. 47(1), 109–148 (2019)

    Article  MathSciNet  Google Scholar 

  4. de Boor, C., Hllig, K., Riemenschneider, S.: Convergence of cardinal series. Proc. Am. Math. Soc. 98(3), 457–460 (1986)

  5. Erb, W.: Graph signal interpolation with positive definite graph basis functions. arXiv preprint arXiv:1912.02069 (2019)

  6. Erb, W.: Semi-supervised learning on graphs with feature-augmented graph basis functions. arXiv:2003.07646v1 [cs.LG] 17 Mar 2020

  7. Feichtinger, H., Pesenson, I.: Iterative recovery of band limited functions on manifolds. Contemp. Math. 137–153, (2004)

  8. Feichtinger, H., Pesenson, I.: A reconstruction method for band-limited signals on the hyperbolic plane. Sampl. Theory Signal Image Process. 4(2), 107–119 (2005)

    Article  MathSciNet  Google Scholar 

  9. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  10. Führ, H., Pesenson, I.: Poincaré and Plancherel-Polya inequalities in harmonic analysis on weighted combinatorial graphs. SIAM J. Discrete Math. 27(4), 2007–2028 (2013)

  11. Haeseler, S., Keller, M., Lenz, D., Wojciechowski, R.: Laplacians on infinite graphs: Dirichlet and Neumann boundary conditions. J. Spectr. Theory 2(4), 397–432 (2012)

    Article  MathSciNet  Google Scholar 

  12. Huang, C., Zhang, Q., Huang, J., Yang, L.: Reconstruction of bandlimited graph signals from measurements. Digital Signal Process. 101, 102728 (2020)

    Article  Google Scholar 

  13. Jorgensen, P.E.T., Pearse, E.P.J.: A discrete Gauss-Green identity for unbounded Laplace operators, and the transience of random walks. Israel J. Math. 196(1), 113–160 (2013)

  14. Linderman, G.C., Steinerberger, S.: Numerical integration on graphs: where to sample and how to weigh. Math. Comp. 89(324), 1933–1952 (2020)

    Article  MathSciNet  Google Scholar 

  15. Madeleine, S., Kotzagiannidis, Pier Luigi Kotzagiannidis, P.L.D.: Sampling and reconstruction of sparse signals on circulant graphs—an introduction to graph-FRI. Appl. Comput. Harmon. Anal. 47(3), 539–565 (2019)

    Article  MathSciNet  Google Scholar 

  16. Marques, A.G., Segarra, S., Leus, G., Ribeiro, A.: Sampling of graph signals with successive local aggregations. IEEE Trans. Signal Process. 64(7), 1832–1843 (2016)

    Article  MathSciNet  Google Scholar 

  17. Mohar, B.: Some applications of Laplace eigenvalues of graphs. In: G. Hahn and G. Sabidussi, editors, Graph Symmetry: Algebraic Methods and Applications (Proc. Montreal 1996), volume 497 of Adv. Sci. Inst. Ser. C. Math. Phys. Sci., pp. 225-275, Dordrecht (1997), Kluwer

  18. Mohar, B., Woess, W.: A survey on spectra of infinite graphs. Bull. London Math. Soc. 21(3), 209–234 (1989)

    Article  MathSciNet  Google Scholar 

  19. Narang, S.K., Gadde, A., Ortega, A.: Signal processing techniques for interpolation in graph structured data. In: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, pp. 54455449 (2013)

  20. Ortega, A., Frossard, P., Kovacevic, J., Moura, J.M.F., Vandergheynst, P.: Graph Signal Processing: Overview, Challenges and Applications. In: Proceedings of the IEEE, pp. 808–828 (2018)

  21. Perraudin, N., Paratte, J., Shuman, D.I., Kalofolias, V., Vandergheynst, P., Hammond, D.K.: GSPBOX: A toolbox for signal processing on graphs. https://lts2.epfl.ch/gsp/

  22. Pesenson, I.: A sampling theorem on homogeneous manifolds. Trans. Am. Math. Soc. 352(9), 4257–4269 (2000)

    Article  MathSciNet  Google Scholar 

  23. Pesenson, I.: Sampling of band limited vectors. J. Fourier Anal. Appl. 7(1), 93–100 (2001)

    Article  MathSciNet  Google Scholar 

  24. Pesenson, I.: Poincaré-type inequalities and reconstruction of Paley-Wiener functions on manifolds. J. Geometric Anal. 4(1), 101–121 (2004)

    Article  MathSciNet  Google Scholar 

  25. Pesenson, I.: Sampling in Paley-Wiener spaces on combinatorial graphs. Trans. Am. Math. Soc. 360(10), 5603–5627 (2008)

    Article  MathSciNet  Google Scholar 

  26. Pesenson, I.Z.: Variational splines and Paley-Wiener spaces on combinatorial graphs. Constr. Approx. 29(1), 1–21 (2009)

    Article  MathSciNet  Google Scholar 

  27. Pesenson, I.Z., Pesenson, M.Z.: Sampling, filtering and sparse approximations on combinatorial graphs. J. Fourier Anal. Appl. 16(6), 921–942 (2010)

    Article  MathSciNet  Google Scholar 

  28. Pesenson, I.Z, Pesenson, M.Z., Führ, H.: Cubature formulas on combinatorial graphs. arXiv:1104.0963 (2011)

  29. Pesenson, I.: Sampling solutions of Schrodinger equations on combinatorial graphs. arXiv:1502.07688v2 [math.SP] (2015)

  30. Pesenson, I.Z: Sampling by averages and average splines on Dirichlet spaces and on combinatorial graphs. arXiv:1901.08726v3 [math.FA] (2019)

  31. Puy, G., Tremblay, N., Gribonval, R., Vandergheynst, P.: Random sampling of bandlimited signals on graphs. Appl. Comput. Harmon. Anal. 44(2), 446475 (2018)

    Article  MathSciNet  Google Scholar 

  32. Schoenberg, I.J.: Notes on spline functions. III. On the convergence of the interpolating cardinal splines as their degree tends to infinity. Israel J. Math. 16, 87–93 (1973)

    Article  MathSciNet  Google Scholar 

  33. Shuman, D.I.: Localized Spectral Graph Filter Frames. arXiv: 2006.11220v1 [eess.SP] (2020)

  34. Shuman, D.I., Faraji, M.J., Vandergheynst, P.: A multiscale pyramid transform for graph signals. IEEE Trans. Signal Process. 64(8), 2119–2134 (2016)

    Article  MathSciNet  Google Scholar 

  35. Strichartz, R.S.: Half sampling on bipartite graphs. J. Fourier Anal. Appl. 22(5), 1157–1173 (2016)

    Article  MathSciNet  Google Scholar 

  36. Tanaka, Y., Eldar, Y.C., Ortega, A., Cheung, G.: Sampling Signals on Graphs. From Theory to Applications. arXiv:2003.03957v4 [ eess.SP] (2020)

  37. Tanaka, Y., Sakiyama, A.: M-channel oversampled graph filter banks. IEEE Trans. Signal Process. 62(14), 3578–3590 (2014)

    Article  MathSciNet  Google Scholar 

  38. Tremblay, N., Borgnat, P.: Subgraph-based filterbanks for graph signals. IEEE Trans. Signal Process. 64(15) (2016)

  39. Tremblay, N., Amblard, P.O., Barthelme, S.: Graph sampling with determinantal processes. In: 2017 25th European Signal Processing Conference (EUSIPCO)

  40. Tsitsvero, M., Barbarossa, S.: Di Lorenzo, Paolo, Signals on graphs: uncertainty principle and sampling. IEEE Trans. Signal Process. 64(18), 4845–4860 (2016)

    Article  MathSciNet  Google Scholar 

  41. Wang, X., Liu, P., Gu, Y.: Local-set-based graph signal reconstruction. In: IEEE Transactions on Signal Processing (2015)

  42. Wang, X., Chen, J., Gu, Y.: Local measurement and reconstruction for noisy bandlimited graph signals. Signal Process. 129, 119–129 (2016)

    Article  Google Scholar 

  43. Ward, J.P., Narcowich, F.J., Ward, J.D.: Interpolating splines on graphs for data science applications. Appl. Computat. Harmon. Anal. 49(2), 540–557 (2020)

    Article  MathSciNet  Google Scholar 

  44. Yazaki, Y., Tanaka, Y., Chan, S.H.: Interpolation and denoising of graph signals using plug-and-play ADMM. In: CASSP 2019—2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019)

Download references

Acknowledgements

MZP was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award DE-SC0020383.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Isaac Z. Pesenson.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pesenson, I.Z., Pesenson, M.Z. Graph Signal Sampling and Interpolation Based on Clusters and Averages . J Fourier Anal Appl 27, 39 (2021). https://doi.org/10.1007/s00041-021-09828-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00041-021-09828-z

Keywords

Mathematics Subject Classification

Navigation