PyGSP: Graph Signal Processing in Python. The PyGSP is a Python package to ease Signal Processing on Graphs. It is a free software, distributed under the BSD license, and available on PyPI. The documentation is available on Read the Docs and development takes place on GitHub. (A Matlab counterpart exists.) The PyGSP facilitates a wide variety of operations on graphs, like computing their Fourier basis, filtering or interpolating signals, plotting graphs, signals, and filters. Its core is spectral graph theory, and many of the provided operations scale to very large graphs. The package includes a wide range of graphs, from point clouds like the Stanford bunny and the Swiss roll; to networks like the Minnesota road network; to models for generating random graphs like stochastic block models, sensor networks, Erdős–Rényi model, Barabási-Albert model; to simple graphs like the path, the ring, and the grid. Many filter banks are also provided, e.g. various wavelets like the Mexican hat, Meyer, Half Cosine; some low-pass filters like the heat kernel and the exponential window; and Gabor filters. Despite all the pre-defined models, you can easily use a custom graph by defining its adjacency matrix, and a custom filter bank by defining a set of functions in the spectral domain.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Emmanouil Krasanakis, Symeon Papadopoulos, Ioannis Kompatsiaris, Andreas Symeonidis: pygrank: A Python Package for Graph Node Ranking (2021) arXiv
- Julien Siebert, Janek Groß, Christof Schroth: A systematic review of Python packages for time series analysis (2021) arXiv
- Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
- Puy, Gilles; Tremblay, Nicolas; Gribonval, Rémi; Vandergheynst, Pierre: Random sampling of bandlimited signals on graphs (2018)