Abstract
The Graph Fourier transform (GFT) is a fundamental tool in graph signal processing. In this paper, based on singular value decomposition of the Laplacian, we introduce a novel definition of GFT on directed graphs, and use the singular values of the Laplacian to carry the notion of graph frequencies. We show that the proposed GFT has its frequencies and frequency components evaluated by solving some constrained minimization problems with low computational cost, and it could represent graph signals with different modes of variation efficiently. Moreover, the proposed GFT is consistent with the conventional GFT in the undirected graph setting, and on directed circulant graphs, it is the classical discrete Fourier transform, up to some rotation, permutation and phase adjustment.
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Chen, S., Varma, R., Sandryhaila, A., Kovačević, J.: Discrete signal processing on graphs: sampling theory. IEEE Trans. Signal Process. 63(4), 6510–6523 (2015)
Cheng, C., Emirov, N., Sun, Q.: Preconditioned gradient descent algorithm for inverse filtering on spatially distributed networks. IEEE Signal Process. Lett. 27, 1834–1838 (2020)
Cheng, C., Jiang, Y., Sun, Q.: Spatially distributed sampling and reconstruction. Appl. Comput. Harmon. Anal. 47(1), 109–148 (2019)
Chung, F.R.K.: Spectral Graph Theory. American Mathematical Society, Providence (1997)
Dees, B.S., Stanković, L., Daković, M., Constantinides, A.G., Mandic, D.P.: Unitary shift operators on a graph. arXiv:1909.05767
Deri, J.A., Moura, J.M.F.: Spectral projector-based graph Fourier transforms. IEEE J. Sel. Top. Signal Process. 11(6), 785–795 (2017)
Domingos, J., Moura, J.M.F.: Graph Fourier transform: a stable approximation. IEEE Trans. Signal Process. 68, 4422–4437 (2020)
Ekambaram, V.N., Fanti, G.C., Ayazifar, B., Ramchandran, K.: Circulant structures and graph signal processing. In: Proceedings of International Conference on Image Processing. pp. 834–838 (2013)
Emirov, N., Cheng, C., Jiang, J., Sun, Q.: Polynomial graph filter of multiple shifts and distributed implementation of inverse filtering. Sampl. Theory Signal Process. Data Anal. 20, Article No. 2 (2022)
Emirov, N., Cheng, C., Sun, Q., Qu, Z.: Distributed algorithms to determine eigenvectors of matrices on spatially distributed networks. Signal Process. 196, Article No. 108530 (2022)
Furutani, S., Shibahara, T., Akiyama, M., Hato, K., Aida, M.: Graph signal processing for directed graphs based on the Hermitian Laplacian. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. pp. 447–463. Springer (2020)
Girault, B., Ortega, A., Narayanan, S.S.: Irregularity-aware graph Fourier transforms. IEEE Trans. Signal Process. 66(21), 5746–5761 (2018)
Kadushin, C.: Understanding Social Networks: Theories, Concepts, and Findings. Oxford University Press, Oxford (2012)
Kotzagiannidis, M.S., Dragotti, P.L.: Splines and wavelets on circulant graphs. Appl. Comput. Harmonic Anal. 47(2), 481–515 (2019)
Kotzagiannidis, M.S., Dragotti, P.L.: Sampling and reconstruction of sparse signals on circulant graphs—an introduction to graph-FRI. Appl. Comput. Harmonic Anal. 47(3), 539–565 (2019)
Le Magoarou, L., Gribonval, R., Tremblay, N.: Approximate fast graph Fourier transforms via multilayer sparse approximations. IEEE Trans. Signal Inf. Process. Netw. 4(2), 407–420 (2018)
Li, Y., Zhang, Z.: Digraph Laplacian and the degree of the asymmetry. Internet Math. 8, 381–401 (2012)
Lu, K.-S., Ortega, A.: Fast graph Fourier transforms based on graph symmetry and bipartition. IEEE Trans. Signal Process. 67(18), 4855–4869 (2019)
Marques, A., Segarra, S., Mateos, G.: Signal processing on directed graphs: the role of edge directionality when processing and learning from network data. IEEE Signal Process. Mag. 37(6), 99–116 (2020)
Ortega, A., Frossard, P., Kovačević, J., Moura, J.M.F., Vandergheynst, P.: Graph signal processing: overview, challenges, and applications. Proc. IEEE 106(5), 808–828 (2018)
Ricaud, B., Borgnat, P., Tremblay, N., Gonçalves, P., Vandergheynst, P.: Fourier could be a data scientist: from graph Fourier transform to signal processing on graphs. C. R. Phys. 20(5), 474–488 (2019)
Saito, N.: How can we naturally order and organize graph Laplacian eigenvectors? In: 2018 IEEE Statistical Signal Processing Workshop (SSP). pp. 483-487 (2018)
Sandryhaila, A., Moura, J.M.F.: Discrete signal processing on graphs. IEEE Trans. Signal Process. 61(7), 1644–1656 (2013)
Sandryhaila, A., Moura, J.M.F.: Discrete signal processing on graphs: frequency analysis. IEEE Trans. Signal Process. 62(12), 3042–3054 (2014)
Sandryhaila, A., Moura, J.M.F.: Big data analysis with signal processing on graphs: representation and processing of massive data sets with irregular structure. IEEE Signal Process. Mag. 31(5), 80–90 (2014)
Sardellitti, S., Barbarossa, S., Di Lorenzo, P.: On the graph Fourier transform for directed graphs. IEEE J. Sel. Top. Signal Process. 11(6), 796–811 (2017)
Segarra, S., Mateos, G., Marques, A.G., Riberio, A.: Blind identification of graph filters. IEEE Trans. Signal Process. 65(5), 1146–1159 (2017)
Shafipour, R., Khodabakhsh, A., Mateos, G., Nikolova, E.: A directed graph Fourier transform with spread frequency components. IEEE Trans. Signal Process. 67(4), 946–960 (2019)
Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process. Mag. 30(3), 83–98 (2013)
Singh, R., Chakraborty, A., Manoj, B.: Graph Fourier transform based on directed Laplacian. In: Proceedings of IEEE International Conference on Signal Processing, Information, Communication. pp. 1–5 (2016)
Stanković, L., Daković, M., Sejdić, E.: Introduction to graph signal processing. In: Vertex-Frequency Analysis of Graph Signals, pp. 3–108. Springer (2019)
Wasserman, S., Faust, K.: Social Networks Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)
Wen, Z., Yin, W.: A feasible method for optimization with orthogonality constraints. Math. Program. 142(1/2), 397–434 (2013)
Yang, L., Qi, A., Huang, C., Huang, J.: Graph Fourier transform based on \(l_1\) norm variation minimization. Appl. Comput. Harmonic Anal. 52, 348–365 (2021)
Zeng, J., Cheung, G., Ortega, A.: Bipartite approximation for graph wavelet signal decomposition. IEEE Trans. Signal Process. 65(20), 5466–5480 (2017)
Zhang, X., He, Y., Brugnone, N., Perlmutter, M., Hirn, M.: MagNet: a neural network for directed graphs. Adv. Neural Inf. Process. Syst. 34, 27003–27015 (2021)
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The authors would like to thank the anonymous reviewers for constructive suggestions and insightful comments for the improvement.
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This work is partially supported by the National Science Foundation (DMS-1816313), National Nature Science Foundation of China (11901192, 12171490), Guangdong Province Nature Science Foundation (2022A1515011060), and Guangzhou Science and Technology Foundation Committee (202201011126).
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Chen, Y., Cheng, C. & Sun, Q. Graph Fourier transform based on singular value decomposition of the directed Laplacian. Sampl. Theory Signal Process. Data Anal. 21, 24 (2023). https://doi.org/10.1007/s43670-023-00062-w
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DOI: https://doi.org/10.1007/s43670-023-00062-w