Graph Signal Processing Book References

[AG07]	Geir Agnarsson and Raymond Greenlaw. Graph Theory: Modeling, Applications, and Algorithms. Pearson/Prentice Hall, 2007.
[AGO16]	Aamir Anis, Akshay Gadde, and Antonio Ortega. Efficient sampling set selection for bandlimited graph signals using graph spectral proxies. IEEE Transactions on Signal Processing, 64(14):3775--3789, 2016. [ DOI ]
[AL13]	Ameya Agaskar and Yue M Lu. A spectral graph uncertainty principle. IEEE Transactions on Information Theory, 59(7):4338--4356, 2013. [ DOI ]
[AO17]	Aamir Anis and Antonio Ortega. Critical sampling for wavelet filterbanks on arbitrary graphs. In Proc. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3889--3893, 2017. [ DOI ]
[BA83]	Peter Burt and Edward Adelson. The laplacian pyramid as a compact image code. IEEE Transactions on Communications, 31(4):532--540, 1983. [ DOI ]
[BBL+17]	Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. Geometric deep learning: Going beyond Euclidean data. IEEE Signal Processing Magazine, 34(4):18--42, 2017. [ DOI ]
[BCM05]	Antoni Buades, Bartomeu Coll, and J-M Morel. A non-local algorithm for image denoising. In Proc. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), volume 2, pages 60--65. IEEE, 2005. [ DOI ]
[BWC+04]	Lowell W Beineke, Robin J Wilson, Peter J Cameron, et al. Topics in Algebraic Graph Theory. Cambridge University Press, 2004.
[BWC+20]	Yuanchao Bai, Fen Wang, Gene Cheung, Yuji Nakatsukasa, and Wen Gao. Fast graph sampling set selection using gershgorin disc alignment. IEEE Transactions on Signal Processing, 68:2419--2434, 2020. [ DOI ]
[BZSL13]	Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203, 2013. [ http ]
[Chu97]	Fan RK Chung. Spectral Graph Theory. Number 92. American Mathematical Society, 1997. [ DOI ]
[CK03]	Mark Crovella and Eric Kolaczyk. Graph wavelets for spatial traffic analysis. In Proc. INFOCOM 2003, 22nd Annual Joint Conference of the IEEE Computer and Communications Societies, pages 1848--1857. IEEE, 2003. [ DOI ]
[CLRS09]	Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, and Clifford Stein. Introduction to Algorithms. MIT Press, 2009.
[CM06]	Ronald R Coifman and Mauro Maggioni. Diffusion wavelets. Applied and Computational Harmonic Analysis, 21(1):53--94, 2006. [ DOI ]
[CMTN18]	Gene Cheung, Enrico Magli, Yuichi Tanaka, and Michael K Ng. Graph spectral image processing. Proceedings of the IEEE, 106(5):907--930, 2018. [ DOI ]
[COHC15]	Yung-Hsuan Chao, Antonio Ortega, Wei Hu, and Gene Cheung. Edge-adaptive depth map coding with lifting transform on graphs. In Proc. 2015 Picture Coding Symposium (PCS), pages 60--64. IEEE, 2015. [ DOI ]
[Cre15]	Noel Cressie. Statistics for Spatial Data. John Wiley & Sons, 2015.
[CRS01]	Dragoš Cvetković, Peter Rowlinson, and Slobodan Simić. An Introduction to the Theory of Graph Spectra. Cambridge University Press, 2001. [ DOI ]
[CS18]	George H. Chen and Devavrat Shah. Explaining the success of nearest neighbor methods in prediction. Foundations and Trends in Machine Learning, 10(5-6):337--588, 2018. [ DOI ]
[CSZ06]	Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien. Semi-Supervised Learning. MIT Press, 2006. [ DOI ]
[CVSK15a]	Siheng Chen, Rohan Varma, Aliaksei Sandryhaila, and Jelena Kovacevic. Discrete signal processing on graphs: Sampling theory. IEEE Transactions on Signal Processing, 63:6510--6523, 2015. [ DOI ]
[CVSK15b]	Siheng Chen, Rohan Varma, Aarti Singh, and Jelena Kovačević. Signal representations on graphs: Tools and applications. arXiv preprint arXiv:1512.05406, 2015. [ http ]
[CVSK16]	Siheng Chen, Rohan Varma, Aarti Singh, and Jelena Kovačević. Signal recovery on graphs: Fundamental limits of sampling strategies. IEEE Transactions on Signal and Information Processing over Networks, 2(4):539--554, 2016. [ DOI ]
[DB13]	Florian Dörfler and Francesco Bullo. Kron reduction of graphs with applications to electrical networks. IEEE Transactions on Circuits and Systems, 60(1):150--163, 2013. [ DOI ]
[DBV16]	Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems, pages 3844--3852, 2016. [ http ]
[Dem72]	Arthur P Dempster. Covariance selection. Biometrics, pages 157--175, 1972. [ DOI ]
[DFC14]	Ismael Daribo, Dinei Florencio, and Gene Cheung. Arbitrarily shaped motion prediction for depth video compression using arithmetic edge coding. IEEE Transactions on Image Processing, 23(11):4696--4708, 2014. [ DOI ]
[DM17]	Joya A Deri and José M. F. Moura. Spectral projector-based graph Fourier transforms. IEEE Journal of Selected Topics in Signal Processing, 11(6):785--795, 2017. [ DOI ]
[DMPP]	Michaël Defferrard, Lionel Martin, Rodrigo Pena, and Nathanaël Perraudin. PyGSP: Graph signal processing in python. [ DOI | http ]
[DR19]	Elisabeth Drayer and Tirza Routtenberg. Detection of false data injection attacks in smart grids based on graph signal processing. IEEE Systems Journal, 2019. [ DOI ]
[DS98]	Ingrid Daubechies and Wim Sweldens. Factoring wavelet transforms into lifting steps. Journal of Fourier Analysis and Applications, 4(3):247--269, 1998. [ DOI ]
[DSOB12]	Marco F Duarte, Godwin Shen, Antonio Ortega, and Richard G Baraniuk. Signal compression in wireless sensor networks. Philophical Transactions of the Royal Society A, 370(1958):118--135, 2012. [ DOI ]
[DTFV16]	Xiaowen Dong, Dorina Thanou, Pascal Frossard, and Pierre Vandergheynst. Learning laplacian matrix in smooth graph signal representations. IEEE Transactions on Signal Processing, 64(23):6160--6173, 2016. [ DOI ]
[DTRF19]	Xiaowen Dong, Dorina Thanou, Michael Rabbat, and Pascal Frossard. Learning graphs from data: A signal representation perspective. IEEE Signal Processing Magazine, 36(3):44--63, 2019. [ DOI ]
[ECO+16]	Hilmi E Egilmez, Yung-Hsuan Chao, Antonio Ortega, Bumshik Lee, and Sehoon Yea. GBST: Separable transforms based on line graphs for predictive video coding. In Proc. 2016 IEEE International Conference on Image Processing (ICIP), pages 2375--2379. IEEE, 2016. [ DOI ]
[ECO20]	Hilmi E Egilmez, Yung-Hsuan Chao, and Antonio Ortega. Graph-based transforms for video coding. IEEE Transactions on Image Processing, 29:9330--9344, 2020. [ DOI ]
[Eld03]	Yonina C Eldar. Sampling with arbitrary sampling and reconstruction spaces and oblique dual frame vectors. Journal of Fourier Analysis and Applications, 9(1):77--96, 2003. [ DOI ]
[Eld15]	Yonina C Eldar. Sampling Theory: Beyond Bandlimited Systems. Cambridge University Press, 2015. [ DOI ]
[EO14]	Hilmi E Egilmez and Antonio Ortega. Spectral anomaly detection using graph-based filtering for wireless sensor networks. In Proc. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1085--1089. IEEE, 2014. [ DOI ]
[EPO17]	Hilmi E Egilmez, Eduardo Pavez, and Antonio Ortega. Graph learning from data under laplacian and structural constraints. IEEE Journal of Selected Topics in Signal Processing, 11(6):825--841, 2017. [ DOI ]
[ETS+20]	Hilmi E Egilmez, Oguzhan Teke, Amir Said, Vadim Seregin, and Marta Karczewicz. Parametric graph-based separable transforms for video coding. In Proc. 2020 IEEE International Conference on Image Processing (ICIP), pages 1306--1310. IEEE, 2020. [ DOI ]
[FFM17]	Giulia Fracastoro, Sophie M Fosson, and Enrico Magli. Steerable discrete cosine transform. IEEE Transactions on Image Processing, 26(1):303--314, 2017. [ DOI ]
[FHT08]	Jerome Friedman, Trevor Hastie, and Robert Tibshirani. Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3):432--441, 2008. [ DOI ]
[Gan00]	Feliks Ruvimovich Gantmacher. The Theory of Matrices, volume 131. American Mathematical Soc., 2000.
[GAO14]	Akshay Gadde, Aamir Anis, and Antonio Ortega. Active semi-supervised learning using sampling theory for graph signals. In Proc. 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 492--501. ACM, 2014. [ DOI ]
[GBR20]	Fernando Gama, Joan Bruna, and Alejandro Ribeiro. Stability properties of graph neural networks. IEEE Transactions on Signal Processing, 68:5680--5695, 2020. [ DOI ]
[GILR20]	Fernando Gama, Elvin Isufi, Geert Leus, and Alejandro Ribeiro. Graphs, convolutions, and neural networks: From graph filters to graph neural networks. IEEE Signal Processing Magazine, 37(6):128--138, 2020. [ DOI ]
[Gir15a]	Benjamin Girault. Signal processing on graphs -- contributions to an emerging field. PhD thesis, Ecole Normale Supérieure de Lyon, 2015. [ http ]
[Gir15b]	Benjamin Girault. Stationary graph signals using an isometric graph translation. In Proc. 2015 23rd European Signal Processing Conference (EUSIPCO), pages 1516--1520. IEEE, 2015. [ DOI ]
[GNO13]	Akshay Gadde, Sunil K Narang, and Antonio Ortega. Bilateral filter: Graph spectral interpretation and extensions. In Proc. 2013 IEEE International Conference on Image Processing, pages 1222--1226. IEEE, 2013. [ DOI ]
[GNO17a]	Benjamin Girault, Shrikanth S Narayanan, and Antonio Ortega. Towards a definition of local stationarity for graph signals. In Proc. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4139--4143. IEEE, 2017. [ DOI ]
[GNO+17b]	Benjamin Girault, Shrikanth S Narayanan, Antonio Ortega, Paulo Gonçalves, and Eric Fleury. GraSP: A Matlab toolbox for graph signal processing. In Proc. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6574--6575. IEEE, 2017. [ DOI ]
[GO19]	Benjamin Girault and Antonio Ortega. What's in a frequency: New tools for graph Fourier transform visualization. arXiv preprint arXiv:1903.08827, 2019. [ http ]
[GOG18]	Vincent Gripon, Antonio Ortega, and Benjamin Girault. An inside look at deep neural networks using graph signal processing. In Proc. 2018 Information Theory and Applications Workshop (ITA), pages 1--9. IEEE, 2018. [ http ]
[GON18]	B. Girault, A. Ortega, and S. S. Narayanan. Irregularity-aware graph Fourier transforms. IEEE Transactions on Signal Processing, 66(21):5746--5761, 2018. [ DOI ]
[GVL12]	Gene H Golub and Charles F Van Loan. Matrix Computations, volume 3. Johns Hopkins University Press, 2012.
[HBM+18]	Weiyu Huang, Thomas AW Bolton, John D Medaglia, Danielle S Bassett, Alejandro Ribeiro, and Dimitri Van De Ville. A graph signal processing perspective on functional brain imaging. Proceedings of the IEEE, 106(5):868--885, 2018. [ DOI ]
[HCO15]	Wei Hu, Gene Cheung, and Antonio Ortega. Intra-prediction and generalized graph Fourier transform for image coding. IEEE Signal Processing Letters, 22(11):1913--1917, 2015. [ DOI ]
[HCOA15]	Wei Hu, Gene Cheung, Antonio Ortega, and Oscar C Au. Multiresolution graph Fourier transform for compression of piecewise smooth images. IEEE Transactions on Image Processing, 24(1):419--433, 2015. [ DOI ]
[HLD+17]	Arman Hasanzadeh, Xi Liu, Nick Duffield, Krishna R Narayanan, and Byron Chigoy. A graph signal processing approach for real-time traffic prediction in transportation networks. arXiv preprint arXiv:1711.06954, 2017. [ http ]
[HSLS16]	Kanghang He, Lina Stankovic, Jing Liao, and Vladimir Stankovic. Non-intrusive load disaggregation using graph signal processing. IEEE Transactions on Smart Grid, 9(3):1739--1747, 2016. [ DOI ]
[HVG11]	David K Hammond, Pierre Vandergheynst, and Rémi Gribonval. Wavelets on graphs via spectral graph theory. Applied and Computational Harmonic Analysis, 30(2):129--150, 2011. [ DOI ]
[ILSL16]	Elvin Isufi, Andreas Loukas, Andrea Simonetto, and Geert Leus. Autoregressive moving average graph filtering. IEEE Transactions on Signal Processing, 65(2):274--288, 2016. [ DOI ]
[Kal16]	Vassilis Kalofolias. How to learn a graph from smooth signals. In Arthur Gretton and Christian C. Robert, editors, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, volume 51 of Proceedings of Machine Learning Research, pages 920--929, Cadiz, Spain, 09--11 May 2016. PMLR. [ .html ]
[KC14]	Eric D Kolaczyk and Gábor Csárdi. Statistical Analysis of Network Data with R. Springer, 2014. [ DOI ]
[KOT+19]	Jiun-Yu Kao, Antonio Ortega, Dong Tian, Hassan Mansour, and Anthony Vetro. Graph based skeleton modeling for human activity analysis. In Proc. 2019 IEEE International Conference on Image Processing (ICIP), pages 2025--2029. IEEE, 2019. [ DOI ]
[KW16]	Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016. [ http ]
[LM19]	Libin Liu and Urbashi Mitra. Policy sampling and interpolation for wireless networks: A graph signal processing approach. In Proc. 2019 IEEE Global Communications Conference (GLOBECOM), pages 1--6. IEEE, 2019. [ DOI ]
[LMGT17]	Luc Le Magoarou, Rémi Gribonval, and Nicolas Tremblay. Approximate fast graph Fourier transforms via multilayer sparse approximations. IEEE Transactions on Signal and Information Processing over Networks, 4(2):407--420, 2017. [ DOI ]
[LO13]	Sungwon Lee and Antonio Ortega. Efficient data-gathering using graph-based transform and compressed sensing for irregularly positioned sensors. In Proc. 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pages 1--4. IEEE, 2013. [ DOI ]
[LO17]	Keng-Shih Lu and Antonio Ortega. A graph laplacian matrix learning method for fast implementation of graph Fourier transform. In Proc. 2017 IEEE International Conference on Image Processing (ICIP), pages 1677--1681. IEEE, 2017. [ DOI ]
[LO19]	Keng-Shih Lu and Antonio Ortega. Fast graph Fourier transforms based on graph symmetry and bipartition. IEEE Transactions on Signal Processing, 67(18):4855--4869, 2019. [ DOI ]
[LOMC20]	Keng-Shih Lu, Antonio Ortega, Debargha Mukherjee, and Yue Chen. Perceptually inspired weighted MSE optimization using irregularity-aware graph fourier transform. In Proc. 2020 IEEE International Conference on Image Processing, 2020. [ DOI ]
[Low04]	David G Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91--110, 2004. [ DOI ]
[LT10]	Brenden Lake and Joshua Tenenbaum. Discovering structure by learning sparse graphs. In Proceedings of the Annual Meeting of the Cognitive Science Society, volume 32, 2010. [ http ]
[LVDV13]	Nora Leonardi and Dimitri Van De Ville. Tight wavelet frames on multislice graphs. IEEE Transactions on Signal Processing, 61(13):3357--3367, 2013. [ DOI ]
[LYSL18]	Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In International Conference on Learning Representations, 2018. [ http ]
[MB06]	Nicolai Meinshausen and Peter Bühlmann. High-dimensional graphs and variable selection with the Lasso. Annals of Statistics, 34(3):1436 -- 1462, 2006. [ DOI ] Keywords: covariance selection, Gaussian graphical models, Linear regression, penalized regression
[MBB17]	Federico Monti, Michael Bronstein, and Xavier Bresson. Geometric matrix completion with recurrent multi-graph neural networks. In Advances in Neural Information Processing Systems, pages 3697--3707, 2017. [ http ]
[MECSDDMO18]	Eduardo Martínez-Enríquez, Jesus Cid-Sueiro, Fernando Diaz-De-Maria, and Antonio Ortega. Directional transforms for video coding based on lifting on graphs. IEEE Transactions on Circuits and Systems for Video Technology, 28(4):933--946, 2018. [ DOI ]
[MFPG17]	Mathilde Ménoret, Nicolas Farrugia, Bastien Pasdeloup, and Vincent Gripon. Evaluating graph signal processing for neuroimaging through classification and dimensionality reduction. In Proc. 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pages 618--622. IEEE, 2017. [ DOI ]
[MHK+18]	John D Medaglia, Weiyu Huang, Elisabeth A Karuza, Apoorva Kelkar, Sharon L Thompson-Schill, Alejandro Ribeiro, and Danielle S Bassett. Functional alignment with anatomical networks is associated with cognitive flexibility. Nature Human Behaviour, 2(2):156, 2018. [ DOI ]
[Mil13a]	Peyman Milanfar. Symmetrizing smoothing filters. SIAM Journal on Imaging Sciences, 6(1):263--284, 2013. [ DOI ]
[Mil13b]	Peyman Milanfar. A tour of modern image filtering: New insights and methods, both practical and theoretical. IEEE Signal Processing Magazine, 30(1):106--128, 2013. [ DOI ]
[MSLR16]	Antonio G Marques, Santiago Segarra, Geert Leus, and Alejandro Ribeiro. Sampling of graph signals with successive local aggregations. IEEE Transactions on Signal Processing, 64(7):1832--1843, 2016. [ DOI ]
[MSLR17]	Antonio G Marques, Santiago Segarra, Geert Leus, and Alejandro Ribeiro. Stationary graph processes and spectral estimation. IEEE Transactions on Signal Processing, 65(22):5911--5926, 2017. [ DOI ]
[MSMR19]	Gonzalo Mateos, Santiago Segarra, Antonio G Marques, and Alejandro Ribeiro. Connecting the dots: Identifying network structure via graph signal processing. IEEE Signal Processing Magazine, 36(3):16--43, 2019. [ DOI ]
[NGO13]	Sunil K Narang, Akshay Gadde, and Antonio Ortega. Signal processing techniques for interpolation in graph structured data. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 5445--5449. IEEE, 2013. [ DOI ]
[NGSO13]	Sunil K Narang, Akshay Gadde, Eduard Sanou, and Antonio Ortega. Localized iterative methods for interpolation in graph structured data. In Proc. 2013 IEEE Global Conference on Signal and Information Processing, pages 491--494. IEEE, 2013. [ DOI ]
[NO09]	Sunil K Narang and Antonio Ortega. Lifting based wavelet transforms on graphs. In Proc. 2009 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pages 441--444, 2009. [ http ]
[NO12]	Sunil K Narang and Antonio Ortega. Perfect reconstruction two-channel wavelet filter banks for graph structured data. IEEE Transactions on Signal Processing, 60(6):2786--2799, 2012. [ DOI ]
[NO13]	Sunil K Narang and Antonio Ortega. Compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs. IEEE Transactions on Signal Processing, 61(19):4673--4685, 2013. [ DOI ]
[OFK+18]	Antonio Ortega, Pascal Frossard, Jelena Kovačević, José M. F. Moura, and Pierre Vandergheynst. Graph signal processing: Overview, challenges, and applications. Proceedings of the IEEE, 106(5):808--828, 2018. [ DOI ]
[OR98]	Antonio Ortega and Kannan Ramchandran. Rate-distortion methods for image and video compression. IEEE Signal Processing Magazine, 15(6):23--50, 1998. [ DOI ]
[PAGR15]	Bastien Pasdeloup, Réda Alami, Vincent Gripon, and Michael Rabbat. Toward an uncertainty principle for weighted graphs. In Proc. 2015 23rd European Signal Processing Conference (EUSIPCO), pages 1496--1500. IEEE, 2015. [ DOI ]
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[PC17]	Jiahao Pang and Gene Cheung. Graph laplacian regularization for image denoising: Analysis in the continuous domain. IEEE Transactions on Image Processing, 26(4):1770--1785, 2017. [ DOI ]
[PEO18]	Eduardo Pavez, Hilmi E Egilmez, and Antonio Ortega. Learning graphs with monotone topology properties and multiple connected components. IEEE Transactions on Signal Processing, 66(9):2399--2413, 2018. [ DOI ]
[Pes08]	Isaac Pesenson. Sampling in Paley--Wiener spaces on combinatorial graphs. Transactions of the American Mathematical Society, 360(10):5603--5627, 2008. [ DOI ]
[Pes09]	Isaac Pesenson. Variational splines and paley--wiener spaces on combinatorial graphs. Constructive Approximation, 29(1):1--21, 2009. [ DOI ]
[PGAR19]	Bastien Pasdeloup, Vincent Gripon, Réda Alami, and Michael G Rabbat. Uncertainty principle on graphs. In Vertex--Frequency Analysis of Graph Signals, pages 317--340. Springer, 2019. [ DOI ]
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[PM03]	Markus Püschel and José M. F. Moura. The algebraic approach to the discrete cosine and sine transforms and their fast algorithms. SIAM Journal on Computing, 32(5):1280--1316, 2003. [ DOI ]
[PM08a]	Markus Püschel and José M. F. Moura. Algebraic signal processing theory: Cooley--tukey type algorithms for DCTs and DSTs. IEEE Transactions on Signal Processing, 56(4):1502--1521, 2008. [ DOI ]
[PM08b]	Markus Püschel and José MF Moura. Algebraic signal processing theory: Foundation and 1D time. IEEE Transactions on Signal Processing, 56(8):3572--3585, 2008. [ DOI ]
[POM17]	Eduardo Pavez, Antonio Ortega, and Debargha Mukherjee. Learning separable transforms by inverse covariance estimation. In Proc. 2017 IEEE International Conference on Image Processing (ICIP), pages 285--289. IEEE, 2017. [ DOI ]
[PPS+14]	Nathanaël Perraudin, Johan Paratte, David Shuman, Lionel Martin, Vassilis Kalofolias, Pierre Vandergheynst, and David K. Hammond. GSPBOX: A toolbox for signal processing on graphs. arXiv preprint arXiv:1408.5781, 2014. [ http ]
[PTGV18]	Gilles Puy, Nicolas Tremblay, Rémi Gribonval, and Pierre Vandergheynst. Random sampling of bandlimited signals on graphs. Applied and Computational Harmonic Analysis, 44(2):446--475, 2018. [ DOI ]
[PV17]	Nathanaël Perraudin and Pierre Vandergheynst. Stationary signal processing on graphs. IEEE Transactions on Signal Processing, 65(13):3462--3477, 2017. [ DOI ]
[RNSC17]	Liu Rui, Hossein Nejati, Seyed Hamid Safavi, and Ngai-Man Cheung. Simultaneous low-rank component and graph estimation for high-dimensional graph signals: Application to brain imaging. In Proc. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4134--4138. IEEE, 2017. [ DOI ]
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[SFV15]	David I Shuman, Mohammad Javad Faraji, and Pierre Vandergheynst. A multiscale pyramid transform for graph signals. IEEE Transactions on Signal Processing, 64(8):2119--2134, 2015. [ DOI ]
[SGO18]	Alexander Serrano, Benjamin Girault, and Antonio Ortega. Graph variogram: A novel tool to measure spatial stationarity. In Proc. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pages 753--757. IEEE, 2018. [ DOI ]
[SH15]	Martin Slawski and Matthias Hein. Estimation of positive definite M-matrices and structure learning for attractive Gaussian--Markov random fields. Linear Algebra and its Applications, 473:145--179, 2015. [ DOI ]
[Shu20]	David I Shuman. Localized spectral graph filter frames: A unifying framework, survey of design considerations, and numerical comparison. IEEE Signal Processing Magazine, 37(6):43--63, 2020. [ DOI ]
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[SKO+10]	Godwin Shen, Woo-Shik Kim, Antonio Ortega, Jaejoon Lee, and HoCheon Wey. Edge-aware intra prediction for depth-map coding. In Proc. 2010 IEEE International Conference on Image Processing (ICIP), pages 3393--3396, 2010. [ DOI ]
[SM00]	Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation. IEEE Transactions on pattern analysis and machine intelligence, 22(8):888--905, 2000. [ http ]
[SM13]	Aliaksei Sandryhaila and José M. F. Moura. Discrete signal processing on graphs. IEEE Transactions on Signal Processing, 61(7):1644--1656, 2013. [ DOI ]
[SM14a]	Aliaksei Sandryhaila and Jose M. F. Moura. Big data analysis with signal processing on graphs: Representation and processing of massive data sets with irregular structure. IEEE Signal Processing Magazine, 31(5):80--90, 2014. [ DOI ]
[SM14b]	Aliaksei Sandryhaila and Jose M. F. Moura. Discrete signal processing on graphs: Frequency analysis. IEEE Transactions on Signal Processing, 62(12):3042--3054, 2014. [ DOI ]
[SMD+20a]	Ljubiša Stanković, Danilo Mandic, Miloš Daković, Miloš Brajović, Bruno Scalzo, Shengxi Li, and Anthony G. Constantinides. Data analytics on graphs part i: Graphs and spectra on graphs. Foundations and Trends® in Machine Learning, 13(1):1--157, 2020. [ DOI ]
[SMD+20b]	Ljubiša Stanković, Danilo Mandic, Miloš Daković, Miloš Brajović, Bruno Scalzo, Shengxi Li, and Anthony G. Constantinides. Data analytics on graphs part ii: Signals on graphs. Foundations and Trends® in Machine Learning, 13(2-3):158--331, 2020. [ DOI ]
[SMD+20c]	Ljubiša Stanković, Danilo Mandic, Miloš Daković, Miloš Brajović, Bruno Scalzo, Shengxi Li, and Anthony G. Constantinides. Data analytics on graphs part iii: Machine learning on graphs, from graph topology to applications. Foundations and Trends® in Machine Learning, 13(4):332--530, 2020. [ DOI ]
[SMMR17]	Santiago Segarra, Antonio G Marques, Gonzalo Mateos, and Alejandro Ribeiro. Network topology inference from spectral templates. IEEE Transactions on Signal and Information Processing over Networks, 3(3):467--483, 2017. [ DOI ]
[SNF+13]	David I Shuman, Sunil K Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Processing Magazine, 30(3):83--98, 2013. [ DOI ]
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