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Found 6 papers, 1 papers with code
Interpolation with the polynomial kernels
We will then study the Reproducing Kernel Hilbert Spaces (or native spaces) of these kernels and their norms, and provide inclusion relations between spaces corresponding to different kernel parameters.
Graph Wedgelets: Adaptive Data Compression on Graphs based on Binary Wedge Partitioning Trees and Geometric Wavelets
We prove that continuous results on best m-term approximation with geometric wavelets can be transferred to the discrete graph setting and show that our wedgelet representation of graph signals can be encoded and implemented in a simple way.
Simple Graph Convolutional Networks
Many neural networks for graphs are based on the graph convolution operator, proposed more than a decade ago.
Kernel-Based Models for Influence Maximization on Graphs based on Gaussian Process Variance Minimization
The inference of novel knowledge, the discovery of hidden patterns, and the uncovering of insights from large amounts of data from a multitude of sources make Data Science (DS) to an art rather than just a mere scientific discipline.
Partition of Unity Methods for Signal Processing on Graphs
Partition of unity methods (PUMs) on graphs are simple and highly adaptive auxiliary tools for graph signal processing.
Semi-Supervised Learning on Graphs with Feature-Augmented Graph Basis Functions
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.
