If you are familiar with conventional spectral analysis, there are higher order spectral analysis. For example before feeding data to your machine learning or A

If you are familiar with conventional spectral analysis, there are higher order spectral analysis. For example before feeding data to your machine learning or AI pipeline, you want transform the waveforms (EM, sound, etc) raw data into higher order spectral to provide more features hence better accuracy. You can use the raw data by simply using the scalar values namely amplitude, time and frequency but the accuracy is much better if you can use the higher order analysis of tine-frequency for examples short time Fourier Transform or spectogram (linear time-frequency), wavelet (time-scale) or the non-linear Cohen class (bilinear time-frequency e.g Wigner-Ville) [1]. But if your data is not a waveform but network (road traffic network, Internet network, sensor networks, etc), similarly you probably better off with higher order graph or hypergraph [2]. In this paper it proposed a linear transformation of graph networks using a new windowed graph Fourier Transform analogous to spectogram, but the spectral in graph here is not the waveform spectrum but the eigenvalues for the matrix representation of the graph. There were discussions few months back in HN on higher order graph or hypergraph approach for analyzing big data [3]. [1] Higher-order spectra analysis : a nonlinear signal processing framework: https://www.semanticscholar.org/paper/Higher-order-spectra-analysis-%3A-a-nonlinear-signal-Nikias-Petropulu/c7d16e16f45925abeb5149c5688dba54a0775853 https://www.semanticscholar.org/paper/Higher-order-spectra-a... [2] Hypergraph: https://en.m.wikipedia.org/wiki/Hypergraph https://en.m.wikipedia.org/wiki/Hypergraph [3] How big data carried graph theory into new dimensions: https://news.ycombinator.com/item?id=38120497 https://news.ycombinator.com/item?id=38120497