# Semi-Supervised Learning on Graphs with Feature-Augmented Graph Basis Functions

arXiv: https://arxiv.org/abs/2003.07646 · HTML: https://mlsanity.com/paper/2003.07646/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [Kernel-Based Models for Influence Maximization on Graphs based on Gaussian Process Variance Minimization](https://mlsanity.com/paper/2103.01575/) (2021)

## Abstract (arXiv)

For semi-supervised learning on graphs, we study how initial kernels in a supervised learning regime can be augmented with additional information from known priors or from unsupervised learning outputs. These augmented kernels are constructed in a simple update scheme based on the Schur-Hadamard product of the kernel with additional feature kernels. As generators of the positive definite kernels we will focus on graph basis functions (GBF) that allow to include geometric information of the graph via the graph Fourier transform. Using a regularized least squares (RLS) approach for machine learning, we will test the derived augmented kernels for the classification of data on graphs.