fast-graph-gp 0.1.1

Graph Random Features for Scalable Gaussian Processes (GRF-GP)

Graph Random Features for Scalable Gaussian Processes (GRF-GP)

GRF-GP is the package for performing fast Gaussian Process (GP) inference on graphs. Internally, it uses Graph Random Features (GRFs) to compute a unbiased & sparse estimate of a family of well-known graph node kernels. It further uses path-wise conditioning to leverage the sparsity of the kernel approximation, enabling you to perform GP model train / inference in $\mathcal{O}(N^{3/2})$ time and $\mathcal{O}(N)$ space complexity.

Examples

For a detailed example of training and using a Graph GP model, refer to the example notebook.

Installation

Install GRF-GP via pip:

pip install grf-gp

Citing Us

If you use GRF-GP, please cite the following papers:

@article{zhang2025graph,
title={Graph random features for scalable Gaussian processes},
author={Zhang, Matthew and Lin, Jihao Andreas and Choromanski, Krzysztof and Weller, Adrian and Turner, Richard E and Reid, Isaac},
journal={arXiv preprint arXiv:2509.03691},
year={2025}
}